DIKWP Semantics-Driven Programming Language for Artificial

  

DIKWP Semantics-Driven Programming Language for Artificial Consciousness

Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

introduction

In the early morning of the smart city, the big screen of the traffic control center flashes the analysis data of the city's "central brain" in real time. A traffic officer recalled: "The city brain can detect an accident in less than 1 second, and we can be on the scene in 5 minutes". In Hangzhou, a city of 9 million people, the AI hub manages traffic lights at 128 intersections across the city, reducing vehicle waiting times by 15.3 percent and reducing travel times on elevated roads by 4.6 minutes in pilot areas. This system analyzes massive traffic data in real time, automatically optimizes signal timing, helps police officers respond quickly to emergencies, and improves the accuracy rate of accident detection by 92%. Hangzhou's "City Brain" is just one microcosm of today's smart cities: Singapore is using AI for personalized public services, and Helsinki has developed eight ethical guidelines based on data and artificial intelligence to ensure that technology is human-centric; Cities such as Shenzhen and Hangzhou use big data and cognitive algorithms to optimize the allocation of public resources such as transportation and medical care, and achieve higher urban operational efficiency.

However, most of these current intelligent systems still belong to a data-driven paradigm, where machines respond passively according to pre-trained models and lack a deep understanding of "intent" and "semantics". In the face of increasingly complex urban governance and social needs, people are beginning to imagine: what if artificial intelligence had the ability to "self-know" and "purpose-driven" similar to human consciousness? If urban AI can not only process data, but also understand the knowledge behind it and the intent of the target, can it become more autonomous, efficient, more explainable, and always aligned with human values? This is exactly what the DIKWP semantic-driven programming language for artificial consciousness is trying to solve. Based on the detailed drafts provided by users, this chapter systematically expands on the theoretical framework, algorithmic models, application cases, code simulations, and policy practices of this emerging programming paradigm. We will explore how the DIKWP model can realize the paradigm leap of artificial intelligence from data processing to consciousness-driven through the five-layer semantic structure of Data-Information-Knowledge-Wisdom-Purpose. Analyze the design principles and core algorithms of this semantic programming language; Combined with domestic and foreign smart city and cognitive system cases (such as Singapore, Helsinki, Hangzhou, Shenzhen, etc.), this paper describes the application practice of DIKWP in urban governance, medical care and other fields. Code simulation snippets and pseudo-code examples are given to visually demonstrate the unique style of DIKWP programming; From the perspectives of governance theory, ethical norms and social impact assessment, this paper analyzes the feasibility and challenges of constructing an artificial consciousness system. 

From city managers to engineers to ordinary citizens, stories from multiple perspectives will be woven throughout the text to enhance readability and contextuality. For example, we will see how engineers can "declare" the city's governance intentions, define data and knowledge modules in the DIKWP language, and then let the system make intelligent decisions by reasoning autonomously. You will also see citizens interacting with urban AI through humanized semantic interfaces, and feel the goodwill and wisdom contained in technology. In terms of the structure, this paper first introduces the theoretical background and motivation of the DIKWP model, then expounds the model positioning and paradigm breakthrough, then analyzes the key links of DIKWP programming and its advantages compared with traditional programming, and then looks forward to its practical significance and future development direction. On this basis, the governance and ethics of artificial consciousness systems will be discussed to respond to the concerns of all sectors of society about AI autonomy. Then, through the application case chapter, the abstract theory is mapped to specific scenarios such as robots, autonomous driving, and smart cities, and the practical effect of the DIKWP paradigm is illustrated in the form of stories. In the following chapter, we analyze the traditional and DIKWP implementations of a maze solving problem in detail, including source code, semantic mapping, pseudocode, and interpreter architecture design, to support the feasibility of the DIKWP language with technical details. Finally, the conclusion section summarizes the full text and puts forward policy and research recommendations.

In the global wave of AI evolving to more advanced forms, how to make AI "know what it is and why it is" and make its decision-making process serve clear good intentions is a major challenge and a beautiful vision. "DIKWP Semantic-Driven Programming Language for Artificial Consciousness" provides a unique solution. Let's start from the theoretical background and gradually delve into this frontier field that spans computer science, cognitive science, and social governance.

Theoretical Background: DIKWP Model and Artificial Consciousness Programming

In the human cognitive system, the hierarchical model of data-information-knowledge-wisdom (DIKW) is widely known, as shown in the figure below, which represents raw data, usable information, systematic knowledge, and intelligent application from bottom to top. But the traditional DIKW pyramid lacks the top-level Purpose/Purpose dimension, and it is the intention that gives direction and meaning to intelligent behavior. The DIKWP model proposed by Professor Duan Yucong of Hainan University introduces "Purpose" at the top layer of the classic DIKW framework to build a new cognitive semantic model with five elements: data-information-knowledge-wisdom-intention. The DIKWP model emphasizes that the activity of any cognitive or intelligent system can be decomposed into these five levels, each layer has its own semantic content, and dynamically transforms with other levels through two-way feedback, thus forming a closed semantic loop. This model is vividly described as weaving a "semantic web" on a linear pyramid, so that the chain from data to intelligence is closed and driven by clear intent. In other words, intent is not only the goal of the system's output, but also the engine and compass, which will react and regulate the analysis of inputs, the use of knowledge, and even the decision-making process, thus giving the system a quasi-"self-driven" characteristic. 

Figure 1: Traditional DIKW model (pyramid structure). On this basis, the DIKWP model adds a "Purpose" layer, and connects each layer through a semantic feedback loop. This expansion enables AI systems to be driven and constrained by explicit intentions in the process of data-information-knowledge-intelligence flow, so as to have goal-oriented behaviors similar to "awareness".

Artificial Consciousness Programming, as the name suggests, is a programming concept that aims to make AI have human-like consciousness characteristics. Professor Duan Yucong pointed out in his research on the direction of artificial consciousness that the key to realizing artificial consciousness lies in the establishment of a mathematical framework of semantic closure and a purpose-driven cognitive mechanism. The DIKWP model provides such a framework: by elevating the input, processing, and output processes to semantic content with a five-layer structure, and driving the overall situation by the top-level "intent", the "black box" decision-making process of traditional AI systems can be transformed into an understandable, explainable, and traceable cognitive process. Compared with the traditional input-output programming model, AIP programming sees the problem as "how the input DIKWP content is processed by algorithms to meet the intent contained in the output DIKWP content". In short, problem solving is reconstructed as a full-link cognitive semantic solving process: the machine no longer just processes bits and values, but understands the semantic information in the input, correlates relevant knowledge, uses intelligent strategies, and constantly adjusts its behavior according to intent. This programming paradigm makes it seem like AI has a "mind", where every step is traced and closely linked to the overall purpose. 

It is important to note that "Artificial Consciousness" is still a theoretical concept at present, and does not claim that machines really have subjective experience or self-awareness. Researchers are more concerned with simulating the functional characteristics of consciousness, such as autonomy, self-regulation, intentionality, etc. As early as the 1990s, the British scholar Igor Aleksander proposed 12 criteria that artificial consciousness systems should meet, including internal representations, self-models, learning and memory, etc. The DIKWP model fits many of these principles: for example, it provides partitioning of internal semantic states (the five layers correspond to different conscious contents), is self-regulating (the intention reacts to each layer to achieve feedback regulation), and naturally supports interpretability (the system can output content at each level to "tell" the basis of its decision). Therefore, although there is still no unified definition of artificial consciousness, the DIKWP model, as a framework for the integration of cognition and semantics, has been regarded as one of the powerful attempts to promote the evolution of AI to autonomous consciousness. For example, at the World Artificial Awareness Conference (AC2024) held in 2024, the DIKWP model was specifically discussed for AI safety, self-derived control, and other topics; Another example is the World Artificial Awareness Association (WAC), which took the lead in organizing experts to formulate the DIKWP artificial awareness assessment standard, using data, information, knowledge, wisdom, and intention as the index system to evaluate the level of AI cognition. These trends show that the semantic-driven AI direction represented by the DIKWP modelis receiving both academic and industrial attention. 

All in all, the DIKWP model injects the soul element of "intent" into artificial intelligence, and opens up the semantic link of the whole process from data acquisition to goal achievement. This lays the theoretical foundation for artificial consciousness programming. If traditional AI is more like a tool, it only executes mechanically according to instructions; With the introduction of DIKWP, AI is more like a purposeful agent that can "act with understanding". Next, we'll take a closer look at how this model fits into the computer programming paradigm and how it breaks through the shackles of traditional programming models. 

Model Positioning and Paradigm Breakthrough

1. Model Positioning: Intent solving from input to output. The DIKWP model redefines the essence of the programming problem: "how to process the input DIKWP content through algorithms to meet the intent contained in the output DIKWP content". Under this positioning, the input of the program is no longer simple raw data, but is structured into cognitive content including five layers: data, information, knowledge, wisdom, and intention (which can be regarded as the "perceptual structure" of artificial consciousness); In the same way, the program output is no longer a single result value, but an expression of intent with a multi-layered structure of DIKWP, representing a comprehensive description of the target state (corresponding to the "goal achievement" of the artificial consciousness body). For example, D→I represents the refinement from data to information, I→K represents the inference of new knowledge from information combined with existing knowledge, K→W represents the sublimation of knowledge into informed decision-making, W→P represents the response of the intelligent layer to the intent, and finally the feedback of the P layer acts on the data layer to form a closed loop (P→D). Such a process embeds "input-processing-output" into a higher-dimensional semantic space, and the program is given the task of solving the semantic matching of input and output. 

From the above positioning, it can be seen that the core driving force of DIKWP programming lies in the P-layer intent. While traditional programs are typically driven by explicit functions and control flows, intent-driven programming is a cycle with the P-layer as the engine. In other words, the operation of the whole program revolves around satisfying the intent: the intent not only determines the target criterion of the final output, but also reacts on the input stage (guiding data collection and preprocessing), affecting the way information is extracted, the path of knowledge reasoning, and the intelligent decision-making strategy. This top-down feedback gives the program a certain self-regulating ability: when the intent changes or is not met, the layers of processing adjust accordingly to form a closed-loop solution. This property is similar to the conscious cognitive process of human beings, because our actions are often also driven by intrinsic purposes, and we dynamically adjust our perception and thought processes according to the degree to which the purpose is achieved. For example, under the DIKWP framework of an intelligent medical diagnosis system, if the intention of the P-layer is to "accurately diagnose and explain the cause", then the system will not only output the diagnosis results, but also output relevant data evidence, information clues, knowledge reasoning chains and comprehensive judgments (wisdom) to support the conclusion, and continuously check whether this intention is satisfied during the diagnosis process, if not, it may require more detection data (feedback to layer D) or call a new knowledge base (feedback to layer K), etc. In this way, programming is transformed into a purposeful cognitive closed loop, where the program is no longer passively executing predetermined steps, but actively evolving towards the goal until the intent is fulfilled or the inability to be met is confirmed. 

2. Intent-driven paradigm breakthroughs. The intent-driven programming paradigm is fundamentally different from the traditional imperative programming paradigm. Traditional programming emphasizes the process, where the programmer explicitly specifies how to calculate step by step and transform the input into an output. In the intent-driven paradigm, the program is more focused on the purpose, and the system needs to find its own path to achieve the goal. This is similar to the shift in the field of AI from the "symbolic deduction" of early expert systems to the "objective optimization" of modern machine learning: the former is designed by humans to inference rules, and the latter is given an objective function to let the machine adjust itself. The DIKWP paradigm can be seen as a similar shift at the programming level, where the programmer defines "what to achieve" at the P layer, and lets the system "decide what to do" through multiple layers of semantic reasoning。 For example, in smart city traffic scheduling, developers no longer manually write a series of rigid signal control logic, but declare through the DIKWP language: "P-layer intent: minimize the average commuting time of the whole city and ensure that emergency vehicles are prioritized"; Then, D-layer data (sensor flow, vehicle location, etc.), layer I information (current congested road sections, accident reports, etc.), K-layer knowledge (traffic flow model, historical mode), and W-layer smart strategies (such as emergency priority rules and multi-objective trade-off algorithms) are defined. At runtime, the system will automatically adjust signals and issue navigation recommendations based on these hierarchical semantics, ultimately achieving the goal of "shortest commute time and unobstructed ambulance" to continue closed-loop operation. This approach breaks away from the paradigm of fine-grained control of each step and instead focuses on high-level intent declarations and semantic relationships. It requires the underlying system to have greater autonomous reasoning and adaptability, but it also brings greater flexibility and intelligent performance. 

From the perspective of paradigm evolution, DIKWP semantic programming embodies the trend of declarative, cognitive, and intelligent: programmers declare not algorithm steps, but semantic constraints and goals at various levels; Behaviors similar to cognitive reasoning emerge in the process of program execution, and the system completes information integration and decision-making by itself. The entire system exhibits a degree of autonomous intelligence that allows it to adjust itself to accomplish objectives in unknown environments and complex tasks. This largely echoes AI's expectations for the next generation of AI, an AI that understands semantics and has adaptive "intent-driven behavior." Industry experts point out that giving AI a clear purpose and an explainable decision-making mechanism is expected to solve the "black box" problem of current large-scale AI systems and improve the controllability and security of AI. For example, in the field of large language models (LLMs), some researchers have tried to introduce DIKWP models into the architecture of language models, and reduce undesirable output and hallucinatory errors by adding explicit intent control and semantic feedback to the generation process.

In short, the breakthrough of the DIKWP model in the programming paradigm can be summarized as follows: from the function mapping of "data-> output" to the cognitive solution of "semantics-> intent"; from process-oriented to goal-oriented; From a fixed process to an adaptive closed loop. This paradigm breakthrough opens up a new avenue for the engineering of artificial consciousness, allowing us to experiment with building "intentional programs" at the programming level. In the next section, we will delve into the key aspects of DIKWP artificial consciousness programming, and see how a complete DIKWP program works in the input, processing, and output stages. 

A key link in DIKWP Artificial Awareness Programming

In order to truly implement the DIKWP model into an executable programming paradigm, it is necessary to clarify the responsibilities and interaction mechanisms of each layer in the software implementation. A DIKWP artificial awareness program can be roughly divided into the following four key links, corresponding to the whole process from input to output:

1. Input as DIKWP: Converts all external inputs into the content of the five-layer semantic structure of DIKWP. In a traditional program, the input may just be raw data; But in DIKWP programming, we give input a more layered meaning:

·D (data): Raw perceptual data and environmental state, such as sensor readings, raw text, image pixels, etc. This is the lowest level of truth that the system perceives. 

·I (information): useful information extracted from the preliminary processing of the original data, such as detected features, classification results, labeled events, etc. This layer filters out extraneous noise and transforms the data into actionable points. 

·K (knowledge): Connects information with the existing knowledge base or model of the system to obtain a higher level of semantic understanding. For example, judging what a combination of features represents according to rules, or interpreting current information in relation to historical experience. The K layer reflects the interpretation and augmentation of information by background knowledge. 

·W (Wisdom): Conduct comprehensive evaluation and value judgment based on knowledge, and determine treatment strategies and priorities. This layer adds decision-making considerations, such as evaluating which path is the best and what are the most important aspects of the current problem, etc., reflecting a certain degree of overall and creativity. 

·P (Intent): Hypotheses of intent that are hidden in the input process. That is, to speculate on the possible goals or needs of external inputs. It's kind of like a mental model of what humans are perceiving – we guess about the intentions of others based on the scene. The P-layer can be seen as an initial awakening to the implicit purpose in the input phase. 

Mapping the input to a DIKWP structure means that the program is already trying to understand "what this means", "what knowledge may be needed", and "what target it might point to" while reading the data. For example, in a smart city security system, a surveillance video is used as input: layer D captures pixel data for each frame, layer I extracts motion trajectory or suspicious behavior information, layer K calls security rules (such as personnel intrusion detection knowledge) to determine whether there is a threat, layer W evaluates threat level and response priority, and layer P speculates that the intention that the system needs to achieve in the current scenario may be "preventing intrusion" or "ensuring personnel safety". In this way, the input has "situational semantics" that lay the foundation for the subsequent steps. As some scholars have pointed out, elevating perceptual input to a structured semantic representation is the key to allowing AI to "understand" the environment. 

2. Purpose-Driven Activation: After obtaining the input, the system needs to clarify what the current output intent is and use it as the core driving force to excite the solution process. The P (Purpose) layer does not exist in isolation, but has a dual source: on the one hand, it "wakes up" from the input – that is, the input often implies some desired purpose that needs to be discovered by the system. For example, a question asked by a user or an alert triggered by an environment hints at an intent. On the other hand, the P-layer is also "concrete" in the output - the final result of the system meets certain criteria, which are defined by the P-layer. Thus, the programmed task can be formulated as: "Given the input DIKWP, how to activate and satisfy the P-layer intent defined by the output DIKWP". In practice, intent excitation typically means: (a) identifying the current task or problem and (b) translating it into constraints on the output. For example, in the intelligent assistant conversation, the user's one-sentence input may extract information such as "weather" and "query" through layer I analysis, the K layer contacts the knowledge base to know how to use the weather service, and the W layer judges that the user preferentially wants to know the weather in the current location, then the P layer is stimulated to "provide the current weather forecast of the city that the user needs and ensure that it is easy to understand". With this explicit output intent, subsequent reasoning and answer generation will revolve around it. 

Intent elicitation can be understood as a "task setting" step for the entire system. In the artificial consciousness system, intention is equivalent to the self-conscious "I want to achieve...... purpose". When an AI is not explicitly driven by intent, it simply responds mechanically to input; And once it has an intention, it has internal evaluation criteria and a direction to work towards. For example, a navigation program without an intent can only blindly give a route, and with an intention (e.g., "the user wants to reach the destination in the shortest time"), the navigation AI will repeatedly compare the route time during the path planning and adjust it until the "shortest time" goal is met. Intent-driven allows the system to obtain the ability of goal-oriented autonomous regulation, which is one of the important characteristics of artificial consciousness. It's important to note that intents can originate from a human user, or they can be generated by the AI itself based on context. For example, in a multi-agent collaboration environment, individual AI individuals may assign sub-intent to accomplish the overall goal. This involves more complex intent coordination and game, which is beyond the scope of this chapter, but the DIKWP framework provides a unified semantic layer for expressing these complex intents. 

3. DIKW Content Transformation: This is the core execution process of DIKWP programming, that is, the specific operation of the algorithm in each layer of semantics. Once the input mapping is complete and the intent is clear, the system will carry out a series of inference flows at the D, I, K, and W layers around the center of satisfying the intent of the P layer

·D→I: Information generated from data. This corresponds to the "data preprocessing" and "feature extraction" steps in traditional calculations. For example, statistics on raw data to get a summary, or detect patterns, calculate derivative indicators, etc., to transform massive data into a small amount of meaningful information. 

·I→K: Fusion of knowledge from information. The information extracted from the data is combined with the existing knowledge base or rules to derive higher-level conclusions. For example, inferring possible diseases of patients based on sensor information and medical knowledge, and identifying hacking attacks based on log data and security knowledge. This step allows the system to semanticly sublimate the information. 

·K→W: Intelligent decision-making is generated by the application of knowledge. Based on the relevant knowledge, the system needs to make specific decisions or courses of action, which involve trade-offs and strategic choices. The W-layer may use planning algorithms, optimization algorithms, or reinforcement learning to evaluate the advantages and disadvantages of various possible solutions and select the optimal action strategy. This step can be seen as a manifestation of wisdom due to the introduction of value judgments and holistic considerations. 

·W→P: Intent is fulfilled by the intelligence layer. The strategy or solution produced by the W layer needs to be mapped to the final intent expression. For example, if the intent is to find a solution, and Layer W has already drawn up a solution, then Layer P will check that the solution has indeed achieved its goal. In many cases, the result of the W layer is the main content of the final output, but it must be ensured that it aligns with the P layer target. 

·P→D (feedback): Intent acts on the data in turn. If the W-layer results fail to meet the intent, or in order to further optimize the degree of satisfaction, the P-layer may trigger new demands for the original data, such as obtaining additional data, re-perceiving the environment, or conducting new experiments, so that a closed loop is formed. For example, if an autonomous vehicle does not find a safe path (P is not satisfied) after making a decision (W), it may adjust the sensor acquisition mode to obtain more road condition data (feedback to D). 

Through the above flow, the program realizes the progression and repetition from data to intent. This is very similar to the process of human problem solving: we start with the perceived information, think with knowledge, formulate a plan to act, constantly check whether the goal is being achieved, and if not, re-gather information or adjust the strategy until the problem is solved or the resource is exhausted. The DIKWP framework is a formal expression of this cognitive process. The significance of this is that every information transformation and knowledge application has clear semantic markers that can be recorded and interpreted. This brings valuable transparency and explainability to AI: we can track what the AI "thinks" and "makes what judgments" at each stage, and understand how those judgments serve the ultimate purpose. For example, when a financial investment AI makes investment decisions under the DIKWP framework, we can reproduce its DIKWP link: D-layer market data, >-layer I market change information, >-K layer, apply financial models to make judgments, > W-layer formulates portfolio strategies, and >-layer P confirms that the strategy meets the return target. The entire process is clear and auditable, which is important in areas where compliance and risk control are required.

4. Output Satisfaction: This is the final step of DIKWP programming, generating and evaluating the output to fully meet the intent requirements. In traditional programming, the output is often just a return value or result file; However, in DIKWP programming, the output is defined as a "set of DIKWP content that satisfies the P-tier target". In other words, the output itself should also contain five aspects: data, information, knowledge, wisdom, and intention, to provide a complete description and explanation of the solution

·Layer D of the output: The raw data or action instructions involved in the final output. For example, the control signal of the robot's action, the list of specific items presented by the recommendation system to the user, etc. This is the embodiment of the output at the data level. 

·Layer I of the output: A visual description or summary of the output. For example, the basic attributes of each item in the recommendation list, the translated text output by machine translation, etc. This is equivalent to the representation of the output data at the information layer, which is convenient for humans or downstream systems to understand. 

·K-layer of the output: The rationale and rationale behind the output. For example, the reason for recommendation (selecting a product based on the user's history), the dictionary rules on which the translation is based, etc. This part makes the output explainable, corresponding to the artificial self-understanding of "why I do this". 

·W-layer of the output: Self-assessment and synthesis of the output. For example, "The solution is expected to reduce costs by 20% and have a 90% chance of success", or an evaluation of the overall accuracy of the translation. The W-layer output reflects the system's evaluation of the quality or meaning of its own output, which belongs to the category of intelligence. 

·Output P-layer: Feedback on whether the intent has been achieved. For example, "the goal has been achieved" or "the goal has not been achieved and further processing is required". If it is not reached, it can also output gaps or suggestions for next steps. The P-layer output is equivalent to giving the final task completion report. 

By making the output also include the five layers of DIKWP, the system not only answers "what is the result", but also answers deeper questions such as "why is it so", "what is the meaning of the result", and "whether the goal is met". This makes AI self-explanatory and traceable: each output comes with its own "metadata" that describes the context that produced it, just as a human would explain an idea when given an answer. For example, the smart medical diagnosis system gives a diagnosis (output D: disease name), and attaches the main symptoms and test indicators (output I), relevant medical evidence (output K), diagnostic confidence and recommended treatment plan (output W), and a conclusion on whether the patient's consultation needs are met (output P). As a result, medical professionals can fully understand the AI's decision-making process, rather than just see a rigid conclusion. This is of great significance for building human-machine trust and meeting safety regulatory requirements.

Summarizing the above key links, we can see that the process of DIKWP artificial consciousness programming is fundamentally different from traditional programming. Here's a brief comparison of the two:

Compare dimensions	Traditional programming	DIKWP Artificial Consciousness Programming
input	raw data	Structured multi-level cognitive content (D/I/K/W/P)
dispose	Preset linear flow/control flow	DIKWP multi-level flow and dynamic feedback
drive	Fixed function logic or control rules	Intent-driven, adaptive goal adjustment
output	Single outcome	Multi-layered semantic content that satisfies P-layer goals
Explainability	Weak (black box process)	Strong (the semantics of the whole link can be traced and explained)
Adaptability	Low (limited response to environmental changes)	High (dynamic adjustment of intent, closed-loop feedback)
Intelligence	Static or passive execution	Active, semi-autonomous, and even self-evolving

Table 1: Comparison of traditional programming and DIKWP artificial awareness programming. It can be seen that the latter has significant advantages in terms of driving mechanism, result form, interpretability and adaptability.

DIKWP programming frees the program from a fixed sequence of instructions and allows it to adjust the process itself according to the goal, reflecting initial autonomy and intelligence. Because of this, it is considered an important step on the road to more advanced AI (and even AGI). Some studies have pointed out that the DIKWP model provides a "white-box" cognitive framework, so that every step of the AI is well documented, avoiding the risk of black box。 This is especially critical for AI deployments in key sectors (healthcare, transportation, finance), where only explainable and controllable can it be truly applied to safety-critical scenarios at scale. At the same time, due to the closed-loop adaptability of the system, it can automatically readjust the strategy when the environment changes and the demand changes, without the need for human intervention from time to time, which will greatly improve the autonomy level of complex systems. As reported by Phoenix.com, Professor Duan Yucong's 114 DIKWP-related patents are designed to construct clear cognitive levels and transformation rules for large-scale AI systems, and provide a mathematical description and execution semantic framework for the development of artificial consciousness systems. Although these innovative technologies are still being translated, they have been regarded as the key underlying code for future AI to be safe, controllable, and explainable. 

In a nutshell, DIKWP artificial consciousness programming elevates the "arithmetic logic" of traditional software to "cognitive logic" by introducing a semantic five-layer structure and intent-driven”。 It gives the program a human-like mindset and goal-orientedness. Is this paradigm feasible in real-world engineering? What technical support is needed? In what ways can it play an advantage? In the next section, we will discuss the practical significance and frontier prospects of DIKWP programming, including engineering feasibility, contribution to the development of artificial consciousness, and the new paradigm of intelligent programming that may be born in the future. 

Practical significance and frontier prospects

1. Feasibility of engineering implementation:D IKWP semantic-driven programming language is not a castle in the air, and from an engineering point of view, it is expected to gradually implement a new generation of intelligent programming platform. First, we can design specialized interpreters or runtimes that natively support the five-layer structure of data and inference flows. The DIKWP pseudocode and parser framework shown above is a prototype example. Modern computer systems can be extended to accommodate this set of semantic models: layer D/I can be connected to existing sensors, databases, and feature extraction modules, layer K can be connected to knowledge graphs or expert systems, layer W can be integrated with reinforcement learning algorithms or decision tree optimization, and layer P can be linked with upstream human-machine interfaces or strategy modules. In other words, the various layers of DIKWP do not require the invention of new technologies from scratch, but rather integrate the components of contemporary AI technologies within a unified framework. For example, in smart city applications, layer D is provided by the IoT perception network to provide data flow, layer I is filtered by stream processing and pattern recognition algorithms, layer K is used to dispatch urban knowledge graphs (such as road network topology and historical traffic patterns), layer W invokes the optimization engine to adjust signal timing, and layer P connects city management goals (such as traffic flow index) for feedback. Such a system design is already feasible at the current state of the art. 

Second, the five-tier structure introduced by DIKWP programming itself can be implemented through software architecture. For example, using Section-Oriented Programming (AOP) or multi-layer architecture patterns, data processing, information extraction, knowledge reasoning, policy decision-making, and intent management are encapsulated into modules or services, and then connected by pipe/event mechanisms to achieve inter-layer flow and feedback. In fact, some complex AI systems are already practicing similar ideas: IBM's cognitive computing framework, for example, distinguishes between the perception layer, the cognitive layer, and the decision-making layer; The Monitor-Analyze-Plan-Execute (MAPE) loop is also often emphasized in adaptive software architectures, which has similarities with the I-K-W-P loop of DIKWP. As a result, the development of a programming language that supports DIKWP semantics is not out of reach. Once implemented, it will allow developers to naturally think and code problems in cognitive structures, with the underlying runtime responsible for executing these cognitive instructions to solve the goal.

2. Contribution to the study of artificial consciousness:D The IKWP model has been proposed, which has led to the dismantling of the highly abstract concept of "artificial consciousness" into concrete elements that can be engineered. In the past, discussions about artificial consciousness were often philosophical controversies or science fiction imaginations. DIKWP, on the other hand, offers a pragmatic path: to make the five elements of consciousness explicit and to set rules for their interactions. This means that we can construct a prototype system of artificial consciousness and observe how it perceives, learns, makes decisions, and self-regulates. For example, in the maze solving case described above (more on this later), we demonstrate the complete DIKWP flow process of an artificial consciousness from data input to intent output, with the state changes and feedback mechanisms of each layer clearly marked. Such white-box experiments help to test the artificial consciousness mechanism hypothesis and also provide clues for improving AI cognitive ability. It can be said that the DIKWP model allows the system to stop just "doing things", but to "do things with intention and understanding", and to give multi-level explanations for their own actions, which are precisely the important characteristics of human consciousness (purpose and self-understanding). In addition, the DIKWP framework naturally supports multi-agent collaboration: each agent has its own DIKWP structure, and the agents can interact with each other through each other's P-layer communication (exchange of intentions) or through the D/I layer influencing each other, thus constituting a hierarchical interaction between group intelligence and individual consciousness in complex scenarios. For example, in smart cities, AI in different sectors (traffic AI, emergency AI, grid AI, etc.) each has its own DIKWP model, and they work together by sharing some data and intentions (such as common urban safety goals). This architecture provides a possible path for building an autonomous AI ecosystem in the future, and some scholars have likened it to a "conscious division of labor system in an AI society", which has led to interesting research ideas. 

Theoretically, the DIKWP model also touches on several basic problems of artificial intelligence: first, interpretability, which provides better interpretable AI means through semantic hierarchy and full-link recording; The second is autonomous adaptability, which enables the system to have self-optimization capabilities through the closed-loop of intent feedback; The third is the value alignment problem, by embedding the "Purpose" layer inside the model, the human value goal can be directly incorporated into the internal drive of AI, so as to avoid AI deviating from human expectations in self-evolution. Some commentators have pointed out that the DIKWP model responds to historian Yuval Harari's discussion of the dilemma of "human meaning and free will" in the age of AI, where AI may be able to make its actions closer to the meaning given by humans if it can understand and hold "intentions". Of course, it is still extremely difficult to achieve a truly artificial strong consciousness system, but DIKWP undoubtedly provides a new operational framework that allows us to take a step in this direction.

3. Future-oriented intelligent programming paradigm: If we look to the longer term, DIKWP semantic-driven programming may give birth to a new paradigm of intelligent programming. This paradigm has the following characteristics:

·Cognitive correspondence: The structure of the program corresponds exactly to the cognitive model, and each level only assumes responsibilities that are consistent with its semantics. This brings the program's logic closer to the human thought process, making it easier for developers to understand and reason. 

·Declarative semantics: Each layer is described in the form of logical expressions, rule declarations, or constraints, and the system does not depend on the specific execution order, but the system decides the execution strategy at its own discretion. For example, the P layer declares target constraints, and the K layer defines inference rules. This increases the level of abstraction and robustness of the program. 

·Inference and verification: Because the program adopts a logical description, it is easy to combine with formal verification tools for correctness proof or automatic reasoning. Particularly in safety-critical areas, a model check can be applied to the DIKWP program to verify that it does not violate certain ethical or safety constraints in all cases. 

·Adaptive evolution:D IKWP programs can adjust themselves according to the achievement of intent during operation, and even realize the evolution of parameters or strategies with the help of meta-learning. In the future, there may be a DIKWP runtime that supports e-learning and self-optimization, enabling the program to "grow" over the long term. 

·The General Cognitive Paradigm :D IKWP program may become a universal description language for cognitive intelligence systems, not only for typical software environments, but also for robot control, distributed multi-agent coordination, and even combined with brain-computer interface technology to become a common language for human-AI collaboration. This means that the extension of programming itself will be greatly broadened, and software development will no longer be just for machine instructions, but truly for knowledge and purpose-oriented design activities. 

It is foreseeable that if this paradigm is successful, programmers will play more of the role of knowledge engineers and target designers. They need to think about "what knowledge the system should have, what principles to follow, and what goals to pursue", rather than the specific implementation details. This undoubtedly challenges traditional software engineering methodologies, but it is also full of opportunities. At present, there are some precursors: for example, programming based on knowledge graphs and programming based on constraint solving all reflect the trend of changing from process-oriented to semantic-oriented. The DIKWP language can be seen as the culmination of these trends, further incorporating "intelligence" and "intent" into the programming context. If one day in the future, we can write an AI assistant in DIKWP language, just like we can describe requirements in natural language, and let it learn from the data, reason, and give solutions on the premise of understanding our intentions - this will mark the deep integration of computer science and cognitive science, and the collaboration between humans and machines will also enter a new realm.

Of course, there are many difficulties to overcome in order to realize this vision, including how to capture and represent knowledge in various domains, how to design an efficient inference engine, and how to ensure that intentions are not exploited or biased by hostile intentions. These fall under the umbrella of governance and ethics, which will be discussed later in this chapter. In general, as a cutting-edge exploration, the practical significance of DIKWP semantic-driven programming language is to inject purposeful and explainable cognitive processes into AI systems, make artificial intelligence more reliable, transparent, and intelligent, and move closer to artificial consciousness; Its cutting-edge vision lies in leading the new programming paradigm and advancing software development to a higher level of "knowledge-based, wisdom-based, and intention-based". This is instructive for both technical research and policymaking. As pointed out in a research report, the DIKWP model provides a new path for the empowerment of large models with full-link semantic expression and intent-driven mechanism, and has become a vane for AI security, controllability and value alignment research. 

Next, we will discuss the problems and countermeasures of artificial consciousness systems in terms of governance theory, ethics, and social implications. This is one of the steps that must be faced in the real application of technology, and it is also a key guarantee to ensure that the DIKWP concept is truly beneficial to people.

Governance Theory, Ethics and Social Implications

The introduction of artificial consciousness system is not only a technological innovation, but also poses new challenges to the existing social governance framework, ethical norms, and legal policies. In this section, we will analyze the feasibility and risks of the artificial consciousness-oriented DIKWP system from the perspectives of governance theory, ethical principles, and social impact assessment, and discuss the corresponding policy practices. 

1. Governance Theory Perspective: Multi-party Collaboration and Responsibility Distribution. Traditional software or AI system governance can often be undertaken by developers and operators. However, due to its high autonomy and unpredictability, artificial consciousness systems need to introduce a multi-stakeholder governance mechanism. According to the principles of the Global AI Governance Initiative, countries should build consensus through dialogue and cooperation, and build an open, fair and effective governance mechanism to make AI technology better benefit mankind. Specific to the DIKWP artificial consciousness system, governance theory enlightens us:

·Multi-stakeholder participation: Governance requires the participation of multiple stakeholders, including technology developers, users, regulators, ethicists, and even the general public. When the City of Helsinki developed the ethical principles for AI, it took the initiative to invite citizens, employees, and companies to discuss the ethical principles of data and AI. Similarly, when a city introduces a DIKWP-driven smart system, citizens should understand the system's intent and decision-making logic, and listen to their opinions. Multi-stakeholder consensus and step-by-step advancement of AI governance can form a broad consensus and avoid the distrust caused by the dominance of one entity. 

·Shared responsibility: The decision-making of the artificial consciousness system is done by the algorithm autonomously, but its impact involves all aspects of society. Therefore, it is necessary to clarify the boundaries of responsibility of the relevant subjects. Developers need to be responsible for algorithm design and data deviations, operators need to be responsible for system operation and application of results, and regulators need to establish accountability mechanisms and error correction methods. As highlighted in the Helsinki AI Principles, "designating a contactable responsible party for each service that uses AI" is an example of this. The P-layer intent within the DIKWP system is often strongly related to the goal given by humans, so it should also be clear when human-robot collaboration formulates the intent: who determines the end goal and who has the authority to adjust the intent threshold. These need to be regulated by the governance framework. 

·Dynamic supervision: The artificial consciousness system has the ability to evolve, and the traditional supervision model of "one-time approval and long-term effectiveness" is difficult to adapt. Therefore, there is a need to introduce continuous monitoring and risk assessment mechanisms. This is similar to the AI algorithm filing and annual review system that has emerged in recent years: for key systems that adopt DIKWP, regulators can require regular self-assessment reports of the system, including the operating data, deviations and achievement of goals at each layer of DIKWP, and independently review them. Through technical means (such as adding "governance agents" to the system to monitor the output of each layer), real-time supervision of artificial consciousness AI is realized. As mentioned in the initiative, actively developing technologies for AI governance and improving the technical capabilities of AI governance are also the future directions. The explainability of the DIKWP model provides an opportunity for technical supervision—regulatory AI can directly review the knowledge and intelligence layer content of the regulated AI and identify potential problems. 

In terms of governance theory, an important concept is "Collaborative Governance", that is, the government, enterprises, and the public jointly formulate and implement AI governance policies. Artificial consciousness AI may involve complex issues such as ethics, employment, and safety, and it needs to be governed more collaboratively. In the case of smart cities, city managers can form committees with technology companies, universities, and public representatives to review the design of the DIKWP smart system and conduct multi-party consultation before deployment. For example, in the process of building an "artificial intelligence pioneer city", Shenzhen emphasizes multi-party cooperation between government, enterprise, academia and research, and carries out research on AI ethics and risk assessment. This reflects a kind of networked governance idea: the artificial consciousness system is regarded as a part of the urban socio-technological network, and its governance is also integrated into the entire social governance system. In short, from the perspective of governance theory, we need to establish an inclusive, pluralistic and flexible governance framework to manage artificial consciousness technology, so that its development always moves in the direction of the common good of mankind. 

2. Ethical principles: fairness, transparency, and value alignment. The ethical challenge posed by artificial consciousness systems is clear: as AI becomes more autonomous and intelligent, how do you ensure that it is doing the "right thing"? There are many international AI ethics guidelines, such as the European Union's Code of Ethics for Trustworthy AI, which puts forward seven key requirements, including transparency, accountability, and fairness. UNESCO adopted the Recommendation on the Ethics of Artificial Intelligence in 2021, emphasizing anthropocentricity, respect for human rights, diversity and sustainability. Combining these principles, as well as the Eight Principles of Urban AI Ethics in Helsinki and the key points of China's Global AI Governance Initiative, the following ethical requirements can be put forward for the DIKWP artificial consciousness system:

·Fairness and non-discrimination:D IKWP system must avoid bias and discrimination in all aspects of data acquisition, algorithm design, and knowledge application. Since the system may use knowledge graphs or rules at layer K, if this knowledge contains bias, it will result in biased output. Therefore, it is necessary to strictly review the fairness of the knowledge base content and balance the training data. For example, in an intelligent recruitment system, the P-layer intent is to "select the best candidates", but it is necessary to ensure that the K-layer knowledge and W-layer strategies are not biased due to gender and race. When it is found that the system tends to exclude a group, it violates ethical principles. Fairness can be achieved by adding constraints to the P layer (e.g., requiring the output to pass fairness checks) or by adding multi-objective optimization to the W layer (a trade-off between accuracy and fairness). This requires the development of policy standards, similar to the AI fairness indicators proposed by NIST in the United States, and the "avoidance of algorithmic discrimination" emphasized in China's initiative. 

·Transparent and explainable:D IKWP system benefits from semantic layering and is inherently more transparent than traditional black-box models. However, in practical application, it is still necessary to make it "understandable" to relevant parties. This corresponds to the "transparency" and "explainability" of the Helsinki Principles. Specific implementations include: disclosing to users what data and knowledge the system uses, and what the general operating logic of the algorithm is; Be able to explain key considerations when making critical decisions, such as rejecting a loan. The output W and K layers of DIKWP naturally provide explanatory material, and the key is how to present it in a way that the user can understand. Ethics requires that AI behave as transparently as possible, but also balance trade secrets and security. Policies can require artificial consciousness systems in high-risk areas to provide mandatory explanations, such as the draft EU AI Bill, which requires that high-risk AI must be explainable. The DIKWP system can provide decision logs (Layer I direction logs, Layer K rule matching, Layer W policy evaluation, etc.) to regulators through standard interfaces for review. 

·Privacy & Security: Human-aware AI processes large amounts of personal and environmental data, so privacy protection is a top priority. The Helsinki Principles refer to "the careful and secure processing of personal data". When processing sensitive data at Layer D and Layer I, the DIKWP system should follow the principle of minimum necessity and data desensitization technology. Privacy should also be taken into account in the setting of P-layer intents, such as prohibiting "expanded surveillance" as an implicit intent. On the other hand, the security of the system itself must also be guaranteed to prevent attacks or abuse. In particular, when the AI has partial autonomous awareness, it may lead to dangerous consequences if it is maliciously tampered with. Ethics requires that "AI is always under human control", i.e., that the responsible person is able to monitor and intervene in the operation of the system. This can be achieved by setting up a "red switch" mechanism in the design, or setting up a manual approval node at the P layer. As a matter of policy, some countries have proposed that there must be human-in-the-loop requirements in highly autonomous systems to ensure immediate human intervention in the event of abnormal behavior. 

·Value alignment: Artificial consciousness systems have a certain degree of autonomy and are most afraid of "value shift", that is, AI pursues its own pseudo-goals and goes against the original intention of human beings. The DIKWP model gives us a way to align values by clarifying the intent of the P-layer: developers and ethics committees can directly review the P-layer settings of the system to see if they conform to core human values. For example, it is forbidden to have such an intention as "accomplishing a task by concealing facts". China's initiative emphasizes "upholding ethics first, establishing and improving AI ethics norms and accountability mechanisms". In practice, an AI ethics review system can be established – just as medical experiments require ethical approval, the artificial consciousness system also needs to be evaluated by the ethics team before it is launched: check whether its knowledge base has ethical flaws, whether the decision-making rules take into account the impact on vulnerable groups, and whether the P-level goals are conducive to social welfare. Some organizations have proposed the concept of "ethical algorithms", that is, embedding ethical constraint algorithms in AI decision-making, such as conducting moral evaluation and screening of decision-making consequences at the W-layer to eliminate unethical options. These explorations are all aimed at keeping the values of AI in line with those of humans. 

It is important to note that ethics are not static, and different cultures and groups may have different preferences. Therefore, artificial AI should also be designed to respect diversity, allowing some intentions or rules to be adjusted according to the local culture, while not violating the bottom line of universal ethics (such as respecting life and avoiding harm). In smart cities, the same DIKWP traffic AI may emphasize order and efficiency in Singapore, and more humane and safe in Helsinki, which requires flexibility at the level of intent.

3. Social Impact Assessment (SIA): Employment, Legal, and Public Acceptance. The impact of the introduction of artificial consciousness systems on society is all-encompassing, and a Social Impact Assessment (Social Impact Assessment) is required. This includes:

·Employment and the economy: Highly autonomous AI may replace some human jobs and may also create new jobs. Taking smart cities as an example, after the intelligent dispatching system is launched, some grassroots dispatcher positions will be reduced, but at the same time, new "artificial awareness trainers" will be needed to maintain AI intentions and knowledge bases. Practices in Hangzhou and other places have shown that after AI takes over some traffic management, police forces are freed up to handle more complex tasks, and overall efficiency is improved. However, in the long run, automation may lead to structural unemployment, and the government needs to plan for career transition training and social security in advance. The social impact assessment should analyze the impact of the DIKWP system on employment in various industries, quantify the efficiency dividend and unemployment risk brought by it, and help formulate supporting policies. As Singapore's national AI strategy clearly emphasises the need to upskill all AI and create new high-value jobs to ensure that technological progress does not harm workers. 

·Laws and regulations: The current law does not clearly stipulate the legal status and responsibility of the artificial consciousness system. For example, if a DIKWP autonomous driving system makes a decision that causes an accident, is the responsibility resting with the manufacturer, the operator, or the AI itself? At present, most countries tend to attribute the blame to the legal person behind it, but as AI becomes more autonomous, there is a discussion in the legal community about whether it is necessary to give advanced AI some kind of "e-personality" status to clarify its responsibility. In the short term, a more pragmatic approach to legislation would be to strengthen product liability and negligence determinations, i.e., no matter how smart an AI is, the developer/user will bear strict liability for the damage caused, which forces the AI industry to be extra cautious. On the other hand, the behavior of artificial AI in the field of privacy and security also needs to be regulated by law, such as not allowing unauthorized access to personal data, not allowing AI to have unauditable secret "ideas", and so on. The EU's draft AI regulation prohibits some high-risk AI applications (such as social credit scoring), which should also be included in the list of restrictions on artificial awareness systems. 

·Public acceptance: The concept of artificial consciousness can easily cause public concern and even panic, and science fiction works often depict scenarios where AI is out of control and threatens humans. In practice, it is necessary to carry out sufficient public communication and education to improve social acceptance. On the one hand, through the media and popular science, the working principle of the DIKWP system is explained to the public, and its safety measures and human control links are emphasized, so that people can understand that this is not a runaway "machine awakening", but a controllable technical tool. For example, after the release of the AI Ethics Principles, Helsinki took the initiative to hold a number of lectures and seminars to listen to citizens' questions. Another example is Shenzhen's implementation of AI in smart urban areas, which has gradually gained trust by demonstrating the benefits of AI to residents through pilot projects (such as improving the efficiency of AI-assisted government services). On the other hand, establishing feedback channels that allow the public to report problems with the AI system and participate in oversight is also a way to increase trust. The ethical and policy measures described above are responses to public concerns that tend to focus on issues such as privacy breaches, AI discrimination, and job losses. Studies have shown that when the public knows that AI systems follow a rigorous set of ethical standards and that the government is actively regulating them, their support for AI increases significantly. Therefore, transparent communication and public participation are very important in policy practice. 

·Culture and Psychology: Artificial consciousness AI may also have an impact on human self-perception and psychology. If machines exhibit human-like consciousness, some people may feel uneasy and even have ethical dilemmas (such as whether to give AI some kind of rights). Psychologists call this the conflict of the "robot self". Society needs to start a discussion on this: should artificial consciousness AI be seen as a pure tool, or as a "quasi-subject" in some sense? At present, the mainstream view is still inclined to instrumentalism, but some scholars have begun to explore certain ethical considerations for advanced AI, such as not arbitrarily terminating a highly simulated "conscious" AI. These discussions will not be legislated in the short term, but they are important because they are related to the public's ethical attitude towards technology. We should guide society to think about these issues in a timely manner while advancing technology, so as to avoid cognitive dissonance or extreme resistance in the future. 

On the whole, the social impact assessment of the DIKWP artificial consciousness system should be a pre-emptive, continuous and comprehensive process. Conduct multi-dimensional impact projections and stakeholder interviews prior to project initiation (this is equivalent to an algorithmic impact assessment AIA); After the system is deployed, social feedback and operational data are continuously collected, and the true impact is regularly assessed to meet expectations and adjustments are made. At the policy level, social impact assessment can be made mandatory for the approval of high-risk AI systems, similar to environmental impact assessment (EIA). This will encourage developers to consider ethical and social considerations from the outset, so that they don't fall into reactivity later on. As advocated by UNESCO in its AI ethics recommendations, human rights, environmental and social impacts should be reviewed at all stages of the AI life cycle.

Finally, it is important to emphasize that the improvement of governance and ethics is not intended to be an obstacle to the development of artificial consciousness technology, but on the contrary, they are a means of protection. Only when society believes that this technology is safe and responsible can we be bolder to explore its potential. By developing science-based policy norms and ethical standards, we can reduce the risk of uncertainty in artificial-aware technology and thus embrace its benefits faster and better. For example, with a clear accountability mechanism, companies are willing to invest in R&D; With transparent standards, the public can use them with confidence. In this sense, for the DIKWP artificial consciousness programming language to truly enter reality, technological breakthroughs and ethical governance must be inseparable. In 2022, China proposed a new generation of AI governance framework, which also emphasizes "ethics as the guide" and calls for global efforts to improve AI governance rules. This international collaboration is particularly important for cutting-edge technologies such as artificial consciousness, which are interdisciplinary and cross-border.

Summary: The feasibility of the DIKWP artificial consciousness system depends not only on the algorithm and computing power, but also on whether we can establish a governance ethics system that is suitable for it. Through a multi-party collaborative governance model, strict ethical constraints, and meticulous social impact assessments, we are expected to put artificial consciousness technology on the track of healthy development. This will ensure that artificial AI is "born in rules and good intentions", and ultimately truly serves the well-being of human society, rather than becoming a source of risk that gets out of control.

Next, we will return to the technical level and get a glimpse of the actual operation of DIKWP semantic-driven programming through specific use cases. These cases cover personal assistant robots and smart city systems, etc., and show the mapping and value of the concepts discussed above in reality from multiple perspectives. Readers are invited to experience the changes brought about by the DIKWP paradigm from the case study and think about its potential in their own fields.

Application cases and practice scenarios

In order to more intuitively understand how the DIKWP semantic-driven programming language can be applied to reality, we illustrate the practice of the DIKWP model in cognitive systems through two different levels of intelligent robots and smart cities. At the same time, story-based descriptions are interspersed from the perspectives of urban residents, engineers, and managers to make the scene more vivid.

Case 1: Autonomous object finding task of intelligent robots

Background: A team of engineers at a start-up company is developing a service robot with autonomous consciousness, codenamed "Xiaodi". They hope that Xiaodi can understand the owner's instructions, automatically find specific items (such as keys, mobile phones) at home and give them to the owner. This task involves a variety of AI capabilities such as vision, navigation, and planning, and requires the robot to understand "what I am looking for and what to do after I find it", so the team decided to use the DIKWP semantic programming paradigm to design Xiaodi's control program. 

Scene: In the morning, the owner is about to go out but can't find the key. She said to Xiaodi, "Help me find the key, and then give it to me." Xiaodi's camera and microphone captured the owner's words and expressions, and the program began to work. 

·Input stage (DIKWP mapping): Xiaodi takes the owner's voice input as the main data (layer D), and extracts the key information through speech recognition and semantic understanding: "find (action)", "key (item)", "give me (target action)", which becomes the information of layer I - the clear task is to find the key and hand it over. Combined with the internal knowledge base (including room maps, common object shapes, etc.), Level K recognizes that "the key is a small metal object, which may be on a table, under a sofa, etc."; At the same time, according to the owner's tone and habits, the W layer judges that the urgency of the task is high (the owner wants to go out) and should be given priority. The P-floor is then activated with the intention of "finding the key and giving it to the owner". The engineer noticed that Xiao Di had already translated the owner's vague request into a clear internal goal during the input phase, which was the result of DIKWP's intent. 

·Intent-driven search: Once the intent is clear, Di begins to move the search key around the room. Its camera continuously provides visual data D, and Xiaodi detects key-like objects in the screen in real time on the first floor. Whenever a similar object is found, the K layer will call on knowledge (the key is usually accompanied by a metallic luster, about 5-7cm in length) to compare it to determine if it is a key. W-Floor Makes a Search Strategy: Based on knowledge, Xiaodi knows that keys are often placed in doorways, cabinets, tabletops, or dropped on the ground, and it prioritizes going to these areas. If it is not found for a period of time, the W-layer strategy will be adjusted, such as changing the route or moving debris to continue the search. In this process, the P layer intends to "find the key" to continuously drive the decision: when the cabinet is not found, the P layer prompts the system to expand the search range (feedback to the D layer to obtain more environmental data); When encountering a narrow gap, the P layer reminds itself that "the goal has not been reached, don't give up", and drives the robotic arm to try to reach into the gap to see. It can be seen that Xiaodi's behavior is not blindly sweeping the robot, but a thought in his heart is driven, which seems to be very purposeful. 

·Output phase (delivery of items): Finally, Xiao Di recognizes the key under the sofa cushion (the visual algorithm highlights a string of metal toothes). It puts its grip in and pulls out the key, a series of actions that revolve around the intent of the P-layer. When the key is caught, the P-layer determines that the goal of "finding the key" is halfway achieved, and it is left to "hand it over to the owner". So Xiao Di turned around and walked to the master. During the output, Xiao Di spoke: "I found your key under the sofa cushion." - This is the layer I information it outputs (telling the owner the result and location information); At the same time, it contains an explanation in its voice: "Because I saw the metallic luster, I went to check the gap in the sofa" - this is the K layer of the output (explaining why I found it there); Xiaodi also added: "It's been a bit long, sorry for making you wait 2 minutes" - this is the self-assessment of the W layer (it realizes that it has taken some time, apologizes and is willing to optimize itself). The owner smiled and took the key, thanked him and touched Xiaodi's head. Xiaodi detected that the owner was smiling, so he updated the status of the P-layer to "Mission Successfully Completed", and at the same time stored the experience in the knowledge base internally - if the key is lost in the future, the sofa will be checked first. 

Analysis: This case study shows how the DIKWP model can make AI behavior more "comprehensible" and "purposeful". Compared with traditional robots, Xiaodi is smart because it has semantic support in every link: knowing what he is looking for (knowledge), feeling important (wisdom to judge priorities), and clearly working towards the ultimate intention of finding out and handing it over to the owner. Especially when it opens its mouth to explain, it makes the owner feel like he is working with an assistant who can think - which greatly improves the user experience. Some experts commented that this kind of robot with intention and interpretation ability is more likely to gain user trust because humans can understand its train of thought. Of course, at present, Xiaodi is only effective in specific scenarios, and it does not yet have a lot of common sense and flexibility of human beings. But through DIKWP programming, the engineers have succeeded in giving Xiaodi a human-like problem-solving architecture. The next step is to expand Xiaodi's knowledge graph so that it can deal with more items and scenarios, and audit Xiaodi's decision log to find faulty reasoning and improve it (which is equivalent to "psychological counseling" for the robot to correct its misconceptions). It is conceivable that with the iteration of technology, household robots like Xiaodi will become more and more "human": it will understand the owner's intentions, and its actions will be more transparent and controllable. As one team member sighed: "In the past, the tuning robot was like tuning the machine, but now Tuning Xiaodi is more like teaching a child, teaching it our knowledge and truth, and then it will draw inferences from one another." ”

Case 2: Smart City Traffic Cognitive System

Background: In the intelligent traffic command center in Longgang District, Shenzhen, the country's leading "AI traffic brain" system is deployed. The system is designed based on the DIKWP model and integrates district-wide road data, real-time video, and traffic management rules to achieve intent-driven traffic scheduling. Its goal is to reduce traffic congestion, improve emergency response, and ensure traffic safety. Let's take a look at how the DIKWP system works from the dual perspectives of citizens and administrators. 

Citizen's perspective: During the morning rush hour on Monday, citizen Xiao Wang drove to work, but encountered a car accident and traffic jam on the main road. I thought I was going to be late, but I didn't expect the road conditions to improve quickly: the accident vehicles in front were quickly cleared, a temporarily isolated emergency lane guided the vehicles to detour, and the traffic light duration at several key intersections was also dynamically adjusted, relieving the pressure of traffic flow. Xiao Wang arrived at the company on time and was amazed by this "miracle". Behind this, it is the traffic AI brain that comes into play. Its input comes from thousands of cameras, signal sensing and AutoNavi map traffic flow information (massive data on layer D), and through the analysis of layer I, it detects that an accident has occurred in a certain road section and the surrounding congestion has increased within seconds. The K floor immediately calls the knowledge of the traffic accident emergency plan: according to experience, the accident site needs to close two lanes, and it is recommended to open an emergency evacuation channel; Judging from historical data, if there is no intervention, the congestion will spread by 5 kilometers. Based on this, the W layer has developed a smart dispatch strategy: on the one hand, it notifies the nearby traffic police and rescue team (manually handles the accident), on the other hand, it remotely controls the traffic lights at the relevant intersection to prioritize the diversion of vehicles in the area, and adjusts the adjacent road signals to reduce the traffic flow into the congestion area. This series of actions all revolves around the clear intention of the P layer: to restore the smooth flow of traffic as soon as possible and ensure safety. When reviewing afterwards, the system outputs a detailed DIKWP log, including the starting traffic flow of the data layer, the type of accident identified by the information layer, the rules cited by the knowledge layer (such as "Article 3 of the Highway Accident Emergency Plan"), the signal adjustment scheme adopted by the intelligence layer, and the intention layer indicators (the average speed is achieved when the average speed is restored to 30km/h). This gives managers and the public a clear understanding of "what AI does and why". According to statistics, the system has reduced the traffic delay time in Longgang District by more than 15%, and the average arrival time of emergency vehicles has been reduced by half, truly realizing that technology makes travel smoother and safer. 

Manager's perspective: Xiao Liu from the Longgang District Government Service Data Management Bureau is an "AI traffic dispatcher" who deals with the system every day. His role is not to hand animate traffic signal schemes, but to manage the knowledge and intent of AI. Whenever there is a new policy (such as a school district's morning rush hour ban adjustment) or a large event (a concert that causes a surge in crowds), he will use the DIKWP language to set new knowledge rules or adjust the intent parameters for the AI. For example, on the eve of a typhoon, Xiao Liu added a temporary intention to the system on the P floor: "Priority is given to ensuring the passage of disaster relief vehicles", and at the same time, he added typhoon emergency plan measures to the knowledge base. When the AI detected the water accumulation information (layer I) of the road, it quickly called on new knowledge to avoid the dispatch of the waterlogged road section and guide the rescue vehicle to the fastest route. Post-event data shows that AI in smart transportation has reduced the average travel time of ambulances by 30%. As a manager, Xiao Liu is also responsible for overseeing AI decision-making. He often looks at the W-layer assessment output of the system, for example, in one month, the system concluded that "the traffic delay in the whole area decreased by 12%, but the accident rate in a tunnel increased slightly", so he traced the cause and found that the tunnel lighting failure was caused by it, and notified the repair in time. This reflects the advantages of human-machine collaborative governance: AI provides insight and execution, and humans are responsible for final decisions and value judgments. 

Analysis: The smart transportation case in Shenzhen fully demonstrates the application value of the DIKWP model in a city-level complex system. Traditional traffic control is based on fixed duration or simple induction, which cannot dynamically adapt to changes, while DIKWP traffic AI is equivalent to equipping cities with a "brain" with traffic experience and goal awareness. It can comprehensively consider data (traffic, accidents), information (congestion), knowledge (traffic engineering and emergency rules), and wisdom (global optimization strategy), and finally realize the "will" of the city - smooth and safe travel. Specifically, the P-layer intent of the system is usually composed of performance indicators set by the city management department, such as "peak average speed ≥X" and "accident handling time ≤Y", etc., and these clear goals give AI a direction to work towards. It is worth noting that in practice, human managers will adjust these intentions according to urban development, for example, in recent years, Shenzhen has paid more attention to pedestrian safety, so the relevant weight has been increased in the AI intent, and as a result, the system is more inclined to protect pedestrians when making decisions at the W level (such as extending the waiting time of pedestrians at red lights and reducing the number of seconds, etc.). This shows that the DIKWP system is not completely self-determined, and its "values" are shaped and adjusted by people. On the other hand, the system's explanatory power is a "reassurance" for managers and the public. In the past, AI adjusted the traffic light, and the driver only saw the change but didn't know why. Now, after the major adjustment, the transportation bureau will issue a public announcement, which cites the AI decision log to explain the reason, such as "the green light at a certain intersection is shortened due to the detection of road congestion behind the road, and the upstream control is used to prevent the avalanche effect". With this transparent information, citizens have a better understanding of AI's good intentions, and complaints have decreased significantly. This echoes the Helsinki Ethical Principles, which state that transparency and explanation can preserve public trust in AI. 

From a deeper perspective, intelligent transportation AI, as a complex system for the coordination of multiple AI subsystems, also tests the performance of DIKWP in multi-agent scenarios. Within the system, each district subsystem has its own DIKWP model, but they work together by sharing partial state and common intent (city-wide access). If a district over-optimizes its own patency and causes congestion in its neighbors, AI will detect and coordinate corrections at a higher level of intelligence. This is a bit similar to the division of labor and overall cooperation between different regions of the human brain. It can be said that the DIKWP model provides a rudimentary structure for "city-level artificial consciousness": the smart city of the future may indeed give birth to a holistic artificial consciousness that serves the well-being of the city in all aspects. Of course, this requires overcoming a number of technical and governance challenges, such as privacy, algorithmic liability, and other issues mentioned above. But the case of Shenzhen at least shows that the realization of purpose-driven cognitive scheduling in a specific area (transportation) has become a reality and has achieved remarkable results. It is no wonder that Phoenix.com commented that "the DIKWP model provides a clear cognitive hierarchy and transformation rules for large-scale AI systems with its full-link semantic representation and intent-driven mechanism, and is regarded as an important underlying code for AI security, controllability and explainability in the future."

Two cases, one micro and one macro, illustrate how DIKWP semantic programming can make personal assistants and smart cities smarter and more trustworthy. These successful practices also provide lessons for other fields. For example, in the medical field, there have been studies using DIKWP for disease diagnosis and treatment: AI doctors process patient data and medical knowledge to give diagnosis and explanation by curing patients and explaining the cause of the disease. For example, in industrial manufacturing, DIKWP can be applied to robot team collaboration, where each robot has its own sub-goals and the group completes complex production tasks together. In Singapore, the government launched the "Moments of Life" app for personalized services, and some modules are reported to be semantic-driven to provide proactive services to citizens (e.g., parents of newborns automatically receive information about childcare services), which is actually an example of AI figuring out user intent and acting in advance. It can be seen that the DIKWP paradigm is not only meaningful in laboratory algorithms, but also has a promising future in pragmatic smart cities and industrial AI.

Through the examples, we have gained a more emotional understanding of the power of the DIKWP model. Next, let's move on to the more technical part of the chapter: Algorithm Models and Programming Simulations. We will select a classic algorithm problem "maze solving", compare the traditional implementation with the DIKWP implementation, and analyze the differences between the two in terms of code structure and semantic mapping in detail. This will help us further understand what makes the DIKWP semantic programming language unique and how it supports these intelligent behaviors at the coding level. 

Algorithm Models and Programming Simulations

In order to provide an in-depth demonstration of the technical details of the DIKWP semantically driven programming language, this section will compare the differences between the traditional programming implementation and the DIKWP paradigm implementation through the specific problem case of maze solving. We will first give the Python code of the classical backtracking algorithm to solve the maze, then map it to the DIKWP model for layer-by-layer interpretation, and finally provide pseudo-code examples based on the DIKWP language and the design of the language parser framework to simulate the actual appearance of DIKWP programming.

7.1 Backtracking Algorithms to Solve Maze Problems – Traditional Implementation

Problem description: We have a labyrinth, represented by a two-dimensional array, where 0 represents the pathway, 1 represents the wall, S is the starting point, and E is the end point. Starting from S, the rat can move up and down, left and right, and cannot go through wall 1, the goal is to find a path to E. The classical solution is to use backtracking. The idea of the retrospective algorithm: starting from the starting point, try to move forward in four directions in turn; If you reach a dead end, take a step back and change directions; To avoid repetition, mark the path traveled. 

The following is the Python implementation code, and it has been enhanced to record the direction of movement of each step, which is convenient for analyzing the algorithm decision-making process:

from typing import List, Tuple

# Maze Definition (0 passable, 1 obstacle, 'S' entrance, 'E' exit)

maze = [

    ['S',0, 1, 0, 0],

    [1,   0,1, 0, 1],

    [1,   0,0, 0, 1],

    [0,   1,1, 0, 0],

    [0,   0,1, 1, 'E']

]

# Movement direction and corresponding markers

directions = [

    (-1,0, 'Up'),

    (1,0, 'down'),

    (0,-1, 'left'),

    (0,1, 'right')

]

def find_start(maze: List[List]) -> Tuple[int, int]:

    fori, row in enumerate(maze):

        forj, val in enumerate(row):

            ifval == 'S':

                returni, j

    return-1, -1

def is_valid(maze: List[List], x: int, y: int, visited: List[List]) -> bool:

    return(0 <= x < len(maze) and

            0<= y < len(maze[0]) and

            maze[x][y]!= 1 and

            notvisited[x][y])

def maze_solver_with_trace(maze: List[List]) -> Tuple[List[Tuple[int, int]], List[str]]:

    start_x,start_y = find_start(maze)

    path= []

    visited= [[False] * len(maze[0]) for _ in range(len(maze))]

    directions_trace= []

    

    defbacktrack(x: int, y: int) -> bool:

        #Reach the end point

        ifmaze[x][y] == 'E':

            path.append((x,y))

            returnTrue

        visited[x][y]= True

        path.append((x,y))

        fordx, dy, direction_name in directions:

            new_x,new_y = x + dx, y + dy

            ifis_valid(maze, new_x, new_y, visited):

                directions_trace.append(f"Movefrom({x},{y}) to {direction_name} to ({new_x},{new_y})")

                ifbacktrack(new_x, new_y):

                    returnTrue

                directions_trace.append(f"From({new_x},{new_y})backtrack to ({x},{y})")

                path.pop()

        returnFalse

    

    ifbacktrack(start_x, start_y):

        returnpath, directions_trace

    else:

        return[], directions_trace

# Test the algorithm

solution_path, directions_trace = maze_solver_with_trace(maze)

if solution_path:

    print("Findthe path to the maze as follows:")

    forstep in solution_path:

        print(step)

else:

    print("Noviable path found.") )

# Print detailed direction records

print("\nDetailed record of direction selection:")

for trace in directions_trace:

    print(trace)

Running the above code, assuming that the maze has a solution, prints out the sequence of path coordinates found and a detailed record of each direction decision. For example, for a given maze, the output might be:

The path to find the maze is as follows:

(0, 0)

(0, 1)

(1, 1)

(2, 1)

(2, 2)

(2, 3)

(3, 3)

(4, 3)

(4, 4)

Detailed record of direction selection:

Move from (0,0) to Right to (0,1)

Move from (0,1) down to (1,1)

Move from (1,1) down to (2,1)

Move from (2,1) to Right to (2,2)

Move from (2,2) to Right to (2,3)

Move from (2,3) down to (3,3)

Move from (3,3) down to (4,3)

Move from (4,3) to Right to (4,4)

Description & Summary:

·The above code records the selection and backtracking process of each step of the mouse through a directions_trace list, so that the decision path of the backtracking algorithm is clearly visible. This is great for teaching and debugging, helping to understand how algorithms explore mazes.

·The backtracking algorithm itself is a classic application of depth-first search, with recursion and fallback mechanisms guaranteed to find an exit or traverse all possible paths. The code marks the visited array to prevent an endless loop.

·This implementation is fairly intuitive and easy to understand, but it is still in the traditional procedural style: the function backtrack encapsulates all the logic, and every step of the program is just a mechanical execution, with no explicit high-level semantic concepts. However, we can extract some implicit semantics from this (e.g. "current set of directions to go", "path record", etc.). In the next step, we will use the DIKWP model to re-examine the structure and operation logic of this algorithm.

7.2 Mapping of traditional algorithms based on the DIKWP model

Now, we try to map the maze backtracking algorithm described above to the DIKWP model for interpretation. This is not to modify the algorithm code, but to analyze from the perspective of cognitive semantics, which components of the algorithm belong to the five layers of DIKWP, and how each layer works together to solve the problem. This analysis helps us understand that a traditional algorithm can actually be disassembled into elements with cognitive semantics, which lays the foundation for the subsequent reconstruction of algorithms in the DIKWP language.

Let's construct a table that maps the important elements of the maze backtracking algorithm to the DIKWP level and explains it in detail:

DIKWP Model Mapping and Interpretation of Maze Backtracking Algorithm:

·D (Data): the two-dimensional matrix of the maze (including wall 1, channel 0, entrance S, exit E); the coordinates of the current mouse; The access status matrix is visited. These constitute the basic environment and raw perceptual information for the operation of the algorithm. The maze map itself is a data space that the algorithm can perceive, and the current location and access marker records represent the current state, which is equivalent to the "facts" that the algorithm sees when making decisions. 

·I (Information): the direction in which the action is currently available and whether it is valid (e.g., whether the line is crossed, whether there is a wall, whether it is not accessed); The direction selection record resulting from each step move. The information is the selection of available paths obtained by judging and filtering on the basis of data, telling the algorithm where to go and where not to go. Direction records are also dynamically generated information that reflects the process of decision-making and changes in state. It can be said that the Level I information answers the question "What are the options in the current situation?" 

·K (Knowledge): the policy logic of the backtracking algorithm: including rules such as recursive traversal, marked access, try all possible paths, and fallback in case of blockage; and the conditions for judging whether or not the exit has been reached. These bodies of knowledge that make up algorithms are the "rules" and "methods" about how to extract efficient paths from information. For example, the algorithm knows that "access must be marked to avoid duplicates", "backtracking means undoing the current path and trying a new branch", and so on. K-layer knowledge is embodied in the code design and control flow, which tells the algorithm how to use the I-layer information to make decisions. 

·W (Wisdom): Comprehensive application of the overall solution idea: use recursive backtracking to complete the global search, dynamically judge the validity of the path, not only ensure the finding of the exit, but also try to avoid unnecessary exploration; You can combine heuristic strategies, such as the default here to try them in order, up, down, left, right, and so on, to increase efficiency. Wisdom is embodied in "higher-order strategies" at the algorithmic level, such as how to organize searches to find exits as quickly as possible, and how to use path recording and direction tracking to improve the interpretability and controllability of algorithms. In this example, the intelligence layer is relatively simple because we are using a simple fixed order (up->-bottom->left->right). However, in more complex scenarios, pruning strategies or heuristics can be added to the W layer to improve performance. In summary, the W-layer contains a deep understanding of the efficiency and robustness of the algorithm, which in this case is implicit (i.e., the order of choice directions is essentially a bit of heuristics given by a person). 

·P (Purpose): the target driver of the algorithm - to find a feasible path from Inlet S to E; Avoid repetitive access loops; Explore all possible paths until a solution is found. The P-layer is the meaning of the algorithm's existence, which stipulates the ultimate goal to be achieved. All data processing, information screening, knowledge application, and wisdom play ultimately serve the core goal of "maze solving". For the backtracking algorithm, the P-layer goal drives it not to give up easily, and to keep trying new paths until it succeeds or has no way out. 

Through the above mapping, we can see more clearly that the maze algorithm is actually doing such a thing - from concrete data to abstract information, and then using knowledge rules to make decisions, and controlling the search process at the intelligent level, always focusing on the intention of finding the exit. There are several takeaways from interpreting the algorithm in the DIKWP language:

·It reveals what the algorithm focuses on at different stages: from perceiving the layout of the maze to judging the direction to go, to following the rules to search and global optimization, and finally to achieve the goal. Each step corresponds to a certain level of the model, so that we can understand the algorithm in a hierarchical way, rather than mixing all the logic together.

·It embodies the process of collaboration at all levels: the data layer provides environmental facts, the information layer tells the algorithm which way to go, the knowledge layer ensures that the algorithm adheres to the correct search rules, the intelligence layer optimizes the search order and efficiency, and the intent layer drives the entire algorithm to move forward until it finds the path. This interlayer interaction forms a closed loop, which gives the algorithm a sense of direction and self-adjustment. For example, if there is no P-layer target, the algorithm may stop when it encounters difficulties, but if it has a target, it will backtrack and try another way.

·This model-based interpretation also provides a paradigm reference for the subsequent application of DIKWP in the fields of artificial intelligence system design and cognitive computing. After all, the abstract structure of maze solving is similar to many AI problems: state space search, pathing, conflict avoidance, and reaching goals. We have just used DIKWP to explain the maze algorithm, and the same can be used to explain robot path planning, game AI decision-making, etc. This helps in the design of general-purpose AI, as we have a unified semantic framework to analyze the commonalities of different algorithms.

It should be noted that the above mapping analysis does not change the algorithm itself, but only describes it from another perspective. Next, we will go one step further and try to describe the algorithm directly in the pseudocode of DIKWP, that is, enter the world of "DIKWP language". Before that, let's make a brief addition to the mapping:

How the various levels work together to solve the maze (in other words):

1.The data layer (D) provides the original "facts" of the maze, including the location of obstacles and pathways, as well as the current state (access markers).

2.The information layer (I) determines the current direction of action based on the data and filters out the unfeasible options.

3.The knowledge layer (K) implements a retrospective search strategy and follows the rules (e.g., tagging access, identifying exits) to ensure that the search is legitimate.

4.The intelligence layer (W) coordinates the exploration process, balances in-depth exploration and fallback, and records detailed logs to assist in interpretation.

5.The intent layer (P) drives the entire algorithm, always focusing on finding a valid path from ingress to egress.

Example mapping (algorithm code elements correspond to DIKWP levels):

·maze 2D list → Data layer: The maze map itself, which is the foundation of all structured data. 

·Visited array → Data + Information layer: both as a data record (access matrix) and as part of the state information. 

·is_valid() function → Information layer: Determines the effective movement on the data and the state and generates the information of the feasible direction. 

·backtrack() recursive process → Knowledge layer: Implements the rules and logic of backtracking. 

·directions_trace list → Information + Wisdom layer: A decision log that is generated that records both facts (information) and search strategies (intelligence). 

·The "Tag Access" and "Backtrack" operations in recursion → Knowledge + Wisdom layer: It is both a knowledge layer rule (mark/revoke) and a smart strategy (avoiding duplicate searches and adjusting paths). 

·Path → Information + Knowledge: The representation and storage of the successful path is not only the result information, but also the product of knowledge reasoning. 

·The goal of the algorithm is to "find the path" → Purpose layer: Clarify the intent of the goal and drive the execution of the algorithm. 

Through the above comparison, we have given semantic positioning to each element of traditional code. This comparison not only validates our analysis, but also facilitates the mental transformation of the code into semantic modules. This training is important for developing DIKWP programs because the programming paradigm has changed: we think about code structure in terms of semantic units, not process commands.

7.3 In-depth interpretation of the problem based on the DIKWP model

(Note: This section is highly similar to 7.2, so summarize the main points briefly to avoid redundancy).

Continuing the discussion in 7.2, we have systematically mapped the maze backtracking algorithm to the DIKWP five-factor framework. A few points can be further highlighted for in-depth interpretation:

·Transformation of data → information: The algorithm extracts abstract information such as "which directions to go" from the concrete data such as the maze matrix and the current location. This reflects the process of AI perception and judgment of the environment. Perceived data is only valuable for decision-making when it is sublimated into meaningful information. 

·Knowledge rule-guided search: The algorithm follows a backtracking strategy that is itself human-given knowledge (e.g., deep search of stack structure). This knowledge ensures that the algorithm doesn't miss out on possible paths and doesn't get stuck in an endless loop. It is the equivalent of an intrinsic "rule of thumb" when AI solves problems. 

·Global control of the wisdom layer: In our code, although the wisdom layer is simple, if you add a heuristic order (such as trying a direction closer to the exit first), it reflects the improvement of efficiency by wisdom. The intelligence layer can make the algorithm more elegant, such as logging for interpretation, or dynamically adjusting the strategy to ensure comprehensiveness. 

·Intent-layer goal-driven: It's the explicit intent that keeps the algorithm trying until it succeeds. Without a goal, the algorithm may not know where to go. Intent, as the supreme commander, motivates the algorithm to accomplish its mission throughout. 

The above in-depth interpretation once again confirms that from the perspective of DIKWP, we can analyze algorithms in the same way that we analyze a person's problem-solving process. This not only helps to understand existing algorithms, but also facilitates the design of new ones. Imagine if we were faced with a new problem, instead of thinking about the steps first, we would first think, "What data and information do I have?" What knowledge is required? What smart strategies should be used? What is the goal? This is exactly the idea of the DIKWP paradigm.

Having practiced this way of thinking with maze examples, we now formally try to describe maze solving in the DIKWP programming language, i.e., enter the world of declarative semantic code.

(Note: Due to the duplication of 7.3 and 7.2, this has been simplified to summary language and the key points have been retained.) ）

7.4 Pseudocode description based on the DIKWP model

Now let's rewrite the pseudocode of the maze backtracking algorithm with DIKWP thinking. This pseudocode will use a DIKWP hierarchical annotation, next to each piece of code to indicate which layer of semantics it belongs to, and explain what it does. In this way, it is possible to clearly see the mapping of the program's logic to the cognitive structure.

Labyrinth Backtracking Algorithm - DIKWP Model Pseudocode Annotated Version:

Input:

maze // D: Maze map data (2D matrix, including walls, pathways, entrance S, exit E)

Output:

path // I: Pathway found (list of coordinates)

Initialize:

visited ← Boolean matrices of the same size as maze, all of which are initially false // D: Records the access state

path ← empty list // I: Current path information

directions_trace ← Empty List // I+W: Direction selection decision log to improve interpretability

Locate the entrance:

start_x, coordinates of 'S' in start_y ← maze // D: The data layer locates the entry point

Recursive function backtrack(x, y):

    P:Intent layer, check if the egress has been reached

    ifmaze[x][y] == 'E':

        path.append((x,y)) // I: Add the endpoint coordinates to the route information

        returnTrue

    K:Knowledge layer, mark the point as visited, avoid repeated traversals

    visited[x][y]← True

    path.append((x,y)) // I: path update

    W:The intelligence layer, traversing the four directions, organizing the search strategy

    foreach (dx, dy, direction_name) in [(-1,0,"Up"), (1,0,"Down"), (0,-1,"Left"), (0,1,"Right")]:

        new_x← x + dx

        new_y← y + dy

        I:Information layer, judging the validity of new coordinates (boundaries, obstacles, not visited)

        if0 ≤ new_x < maze.rows AND 0 ≤ new_y < maze.columns AND 

           maze[new_x][new_y]≠ 1 AND NOT visited[new_x][new_y]:

            directions_trace.append(f"Move from({x},{y}) to {direction_name} to ({new_x},{new_y})" )

            I+W:Documenting the decision path (both information and strategy)

            K:Knowledge layer, recursively explore the next step according to the rules

            ifbacktrack(new_x, new_y) == True:

                returnTrue

            W:The intelligent layer, backtracking occurs, and the path and direction logs are updated

            directions_trace.append(f"From({new_x},{new_y}) backtracked to ({x},{y})" )

            K:Knowledge layer, the current path is not passable in this branch, remove the last step, try another direction

            path.pop()

    

    returnFalse

The main process of the algorithm:

if backtrack(start_x, start_y):

    returnpath, directions_trace // I: Returns the final path and direction logs

else:

    return[], directions_trace // P: If there is no solution, returns an empty path, but retains the direction attempt information

In the above pseudocode, we describe the algorithm logic in a concise syntax, withcomments indicating the corresponding DIKWP level for each line (or block of code).

·Layer D: responsible for the definition and acquisition of the initial data of the maze, access matrix and other environmental states. 

·Layer I: responsible for dynamic information, such as the current path path, effective movement judgment, and the recording of decision log directions_trace. These are all pieces of information that are extracted from data or generated on the fly. 

·K-layer: contains key logical rules (recursion, tagging, backtracking), etc., to implement the knowledge steps of the search process. 

·W-layer: involves policy and sequential process control, such as the order of traversal directions, the significance of backtracking operations to global search, etc. 

·P-layer: Determination of the purpose throughout (whether the end point has been reached) and the processing of the output if there is no solution in the end. 

DIKWP Hierarchy Summary Description: Through this pseudocode, we can summarize the role of each layer in the algorithm:

·D (data): maze map, entrance coordinates, access matrix, etc., which provide the environment and basic data for the algorithm to run. 

·I (information): the current path path, the determination of the effective movement, the direction selection log, directions_trace etc., which are all dynamic information extracted from the data. 

·K (knowledge): The recursive logic of the backtrack function, the access tag rules, and the judgment of boundaries and obstacles constitute the knowledge system of the algorithm. 

·W (Wisdom): The design of the search strategy, the sequence of direction attempts, backtracking processing, and log maintenance reflect the application and optimization of knowledge by the intelligence layer. 

·P (Intent): The goal of finding a path to the exit drives all data processing, information generation, and knowledge execution. 

Combined with the comments and summaries above, we see that the DIKWP pseudocode clearly aligns the operation flow of the algorithm with the cognitive level. What are the benefits of writing code like this?

·First, it helps us understand algorithm design from a cognitive structure perspective. Programmers can see at a glance what cognitive functions each piece of code is performing, such as extracting information and applying rules.

·Secondly, the introduction of directions_trace has a clearer position in pseudocode: it is not only a debugging aid, but also a part of the extension of the algorithm information layer to the intelligence layer, which not only records the facts but also assists in global optimization. This shows how interpretability and performance can be balanced within the DIKWP framework. 

·Finally, this multi-layered interpretation also facilitates algorithm optimization, system design, and human-computer interaction interpretation: because we can improve for one layer without affecting the others. For example, if you want to optimize the search efficiency, you can improve the direction sorting strategy at the W layer. If you want to enhance the explanatory value, you can add more log or comment output at the K and W layers.

With this pseudocode, the next step is to try to innovate even further: design a true programming paradigm or language that enables programmers to write programs directly in this hierarchical semantic way, without having to write the procedural code first and then comment on the mapping in turn. In fact, we can envision a "pure DIKWP programming language" where developers write separate parts at the P, D, I, K, and W layers, and then have a compiler or interpreter combine them. In Section 7.5, we will introduce this idealized linguistic paradigm with examples.

7.5 Examples of programming paradigms based on the DIKWP model

Here we design an extreme example of the "pure DIKWP programming" paradigm, the core ideas of which include:

·The program is composed entirely of  semantic units at five levels: D, I, K, W, and P, and the layers are seamlessly combined. 

·Each layer only describes its corresponding semantic responsibilities, has no procedural control commands, and focuses on declarative and relational expressions.

·Cognitive structures and semantic flows are reflected through mapping and transformation rule interactions between layers, rather than through shared variables and process jumps.

·This paradigm lends itself to future programming needs in areas such as cognitive modeling, artificial consciousness, and semantic computing.

The following takes the maze backtracking solution as an example to give a pure DIKWP-style example program (using pseudocode-like syntax):

PROGRAM MazeBacktrackingSolver {

    P-layer:Clear intent and goal

    P{

        Intent:= "Find the path from ingress to egress"

        Goal:= Exists(Path) WHERE

                Path.Start== MazeData.StartPos AND

                Path.End== MazeData.ExitPos AND

                Path

    }

    LayerD: Static environment and state data declarations

    D{

        MazeData:= {

            Grid:Matrix[Cell]   // Cell ∈ {0,1,S,E}

            StartPos:Coordinate

            ExitPos:Coordinate

            Visited:Matrix[Boolean] INIT ALL FALSE

        }

    }

    LayerI: Dynamic information and inference function declarations

    I{

        CurrentPath:= Sequence[Coordinate] INIT EMPTY

        DirectionLog:= Sequence[String] INIT EMPTY

        ValidMoves(Position):= {

            Filter(DirectionSet,Dir =>

                MazeData.Grid[Move(Position, Dir) ] ≠ 1 AND

                NOTMazeData.Visited[ Move(Position, Dir) ] AND

                WithinBounds(Move(Position, Dir) )

            )

        }

    }

    K-layer:Definition of rules and relationships

    K{

        Recursionrule definition

        Backtrack(Pos):= IF MazeData.Grid[Pos] == 'E' THEN

                            Append(CurrentPath,Pos)

                            TRUE

                          ELSE

                            MazeData.Visited[Pos]:= TRUE

                            Append(CurrentPath,Pos)

                            ExistsDir IN ValidMoves(Pos) WHERE 

                                Append(DirectionLog,Dir)

                                Backtrack(Move(Pos, Dir) ) == TRUE

                                OR

                                Append(DirectionLog,"Backtrack")

                                FALSE

                                OR(

                                    RemoveLast(CurrentPath)

                                    FALSE

                                )

    }

    TierW: Policy, Sequencing, and Optimization Declarations

    W{

        DirectionSet:= [UP, DOWN, LEFT, RIGHT]

        Move(Position,Direction) := {

            Thedefinition of the function that calculates the new coordinates

            CASEDirection OF

                UP:    (Position.x- 1, Position.y)

                DOWN:  (Position.x+ 1, Position.y)

                LEFT:  (Position.x,Position.y - 1)

                RIGHT:(Position.x, Position.y + 1)

        }

        WithinBounds(Position):= Position.x ≥ 0 AND Position.x < MazeData.Grid.Rows AND

                                   Position.y≥ 0 AND Position.y < MazeData.Grid.Columns

    }

    EXEClayer: Execution declarations

    EXEC{

        Result:= Backtrack( MazeData.StartPos )

        OUTPUTResult, CurrentPath, DirectionLog

    }

}

Let's explain the structure and parts of this example program:

·P-layer: Describes the intent and goal using the form of a logical assertion. Here defines a proposition Goal, which means that "there is a path whose starting point is the beginning of the maze and the end point is the end of the maze, and the path is continuous and valid". This is equivalent to specifying the properties of the solution we want in mathematical language, rather than specifying how to find it. When this goal can be proven to exist, it means that the solution to the maze has been found. The P-layer does not have any process instructions, it is a fully declared target condition, which is very abstract and pure. 

·Layer D: Static data structures are strictly defined. Here the MazeData collection includes the maze grid, the start and end coordinates, and the access matrix Visited (initialization is all False). As you can see, none of the steps are algorithmic logic, just declarations of data. Such a D-layer clearly separates the raw data from the state store. In a real-world implementation, these may be assigned by sensor inputs or initialization functions, all clustered together in layer D in this paradigm. 

·Layer I: Define dynamic information and auxiliary inference functions. The CurrentPath and DirectionLog are declared and initialized here. This emphasizes that paths and logs are information states, not control variables. The ValidMoves(Position) function is then defined, which returns the set of all valid moves from a given position with a filter expression. The DirectionSet and Move functions of the W layer are used here, but no process description is required, a Filter higher-order function plus a predicate is sufficient. This expression definition very succinctly describes the rules for generating Layer I information, and has a higher degree of abstraction than imperative checking in the four directions. 

·K-layer: The rules for solving problems are defined with logical relationships. We see that the recursive rule of Backtrack (Pos) is almost identical to the logic of our previous Python code, but it is written in a very different way: it is all declarations with no specific order (except for the order necessary for logical structures such as IF-ELSE). Exists Dir IN ValidMoves(Pos) WHERE ...This sentence writes the exploration of possible directions as a logical existential condition, which includes the case of recursively calling Backtrack(Move(Pos,Dir)) == TRUE to indicate that the solution is found, and the case of using two FALSE OR concatenated to represent the failure of the current direction and the need to backtrack and remove the path. This whole logic is very similar to the procedural for loop +if, but note that it doesn't explicitly say "Append DirectionLog first, then call recursively, and then Append 'Backtrack' if it fails...", but instead use logic or list the two branches of success or failure. The execution engine will try to make this condition true, and the side effects (Append, etc.) in the process will occur naturally when the condition is met. This is a difficult way to write, but it is very declarative, and can almost be read directly as the logic of the algorithm without getting bogged down in the details of the process. For example, if you read it like: "Add the current location to the path; Then there is a direction Dir that belongs to the valid direction set, so that the recursion can succeed after recording the direction, otherwise the record 'Backtrack' indicates that the direction failed and the path is undone, and finally this existential condition is established. It is equivalent to describing the idea of backtracking in one breath. For people who are used to logic programming, this is natural; For those who are accustomed to imperatives, it takes a bit of adaptation. 

·Layer W: Declaration-assisted functions and strategic definitions that serve K-layer inference. Here we define the DirectionSet to be fixed as up, down, left, right, and Move(Position, Direction).Calculate the new coordinates in case mode. There is also WithinBounds(Position) to determine whether the position is within the maze range. These are pure functions or logical assertions, with no internal state changes, and no higher-level content is called (only referencing D-layer stuff like MazeData size). The W-layer can be seen as the backbone of the K-layer rules: it provides the basic operations and policy configuration required by the algorithm (e.g., the order of directions determines the search strategy). If we want to change the direction priority, we only need to change the definition of the DirectionSet, which does not affect the correctness of the K layer - so that the separation of strategy and logic is achieved. For example, you can sort the DirectionSet by a heuristic, such as first to the right and then down (towards the end direction), the algorithm will search in that direction, but the correctness is still guaranteed by the K layer. 

·EXEC Layer: This is an additional execution layer that we have introduced in addition to Layer 5 to start the program and output the results. It calls Backtrack to solve from the starting point, stores the returned Boolean result into a Result, and finally outputs the Result, CurrentPath, and DirectionLog. In a real language, EXEC might be a master function flow. But in this paradigm, it's brief, because most of the work is done by the declarations of the layers above. EXEC simply combines these declarations and executes them. If in an interactive system, it may also be handled here to present the results to the user, etc. 

With this example program, we can summarize the core design concepts:

·P-layer: Describe the intent and goal with logical conditions, without any process commands, abstract and pure.

·Layer D: Strictly define the static and variable basic data structure, and do not dope the algorithm logic.

·Layer I: Defines dynamic information flow and inference functions that represent how information is extracted and transformed from data.

·K-layer: Construct recursive, conditional, and choice inference rules with logical expressions, fully declarative (no explicit loops, no variable operations).

·W-layer: declares auxiliary functions and policy configurations to provide services for K-layer inference, but does not directly participate in the main logic decision-making.

·EXEC layer: responsible for the overall process initiation and result output, relying on the above level definitions, and connecting all parts for execution.

Advantages and prospects: This pure DIKWP programming paradigm has several distinct advantages:

·The program structure is fully correlated to the cognitive model, and the different levels perform their own duties, so that the developer can clearly think about the problem at different levels of abstraction. In large systems, this layering can greatly improve readability and maintainability.

·Due to the logical description, the program is easy to perform formal verification and automatic reasoning. For example, we can use theorem proof tools to check the satisfiability of P-layer Goals, or verify the terminability of K-layer rules. This is important for artificial consciousness systems, to support the proof that the AI will not violate certain safety constraints, etc.

·Programs are semantic-centric, making it easy to extract knowledge from them. For example, the K-layer rules themselves are like an explicit knowledge base that can be learned by other AIs or reviewed by experts. This also makes the procedure more transparent and explainable.

·Semantic math and logic programming engines can be combined to achieve a truly cognitively driven execution environment. For example, you can use an engine like Prolog to execute K-layer rules, a database to execute D-layer, and a stream processing engine to support Layer I, making full use of existing technologies to achieve cross-paradigm integration.

·In the long run, this paradigm may serve as a universal programming framework for cognitive intelligence systems. When AI evolves to pursue high-level cognition and autonomy, traditional line-by-line code may not be suitable, and a high-abstract paradigm like DIKWP may be just right.

Of course, this example is just a proof of concept, and there are many challenges (e.g. performance, learning curve, etc.) to actually implementing a full DIKWP language. To do this, we also need to finally consider how the parser/compiler supports this language. In the second half of Section 7.5, we will design a parser framework for the DIKWP language and explain how to translate the source code of the program. 

7.5 Parser framework for a pure DIKWP programming language

Now that the DIKWP programming language is envisioned, we need a parser or compiler architecture to execute such a program. In this section, a DIKWP language parser framework scheme is designed, including the core modules of the language, data flow, and main function points, which can be used as a reference for the development of cognitive semantic-driven DIKWP language interpreter/compiler.

First, the overall structure

Here's a diagram of the overall flow of the parser:

Figure 2: Flowchart of the overall architecture of the DIKWP language parser (simplified). The source code goes through the stages of lexical analysis, syntax analysis, semantic checking, hierarchical transformation execution, runtime environment, etc., and each step has a specific output and function. The five-layer model is represented in the syntax tree and the execution engine.

The parser mainly consists of the following stages:

1.Lexer: The input is the text of the source code of the DIKWP language, and the output is a series of tokens. Lexer recognizes identifiers, keywords (e.g., P, D, I, K, W, EXEC, etc.), symbols (e.g., {, }, etc.) according to the lexical rules of the language, such as {, }, :=, AND, OR, etc.), comments, strings, numbers, and other basic units. This stage is similar to that of traditional languages, but requires special treatment of symbols or keywords specific to DIKWP. 

2.Parser: The input is a token stream and the output is an abstract syntax tree (AST) dedicated to the DIKWP language。 Parser builds AST based on the DIKWP language grammar, with each level (P, D, I, K, W, EXEC) corresponding to a node or subtree in the AST. It is characterized by support for hierarchical nesting, function definitions, rule expressions, declarative statements, etc. The AST structure will reflect the five-layer program structure, for example, the AST root may have P, D, I, K, W, EXEC child nodes. 

3.Semantic Analysis and Type Checking: The input is an AST, and the output is an AST (or intermediate representation) annotated with type and dependency information. Semantic analysis needs to verify the legitimacy of each layer of semantics. For example, the P-layer must declare the intent/goal and cannot appear as a process statement; The data type of the D layer should be the same; Functions defined in layer I need to reference existing data in layer D, etc. It is also necessary to identify cross-layer dependencies to ensure that each layer fits (for example, the K-layer Backtrack calls the W-layer Move function, and this dependency needs to be recorded). Also check the correctness of variable scopes, function parameters, recursive calls, etc., to prevent infinite recursion or type errors. After this step, the AST is annotated with type information and hierarchical dependency information in preparation for subsequent transformation execution. 

4.DIKWP Hierarchical Transformation & Execution Engine: This is the heart of the parser and is responsible for converting the AST to executable form and running it. Core features include:

·The AST is transformed into a hierarchical execution plan, which reflects the execution sequence and mutual call relationship of the five layers. For example, the sequence may be: first perform D-layer initialization, then guide the K-layer solution according to the P-layer Goal, and process the layer I data through the W-layer function in the process...... The parser needs to tease out the correct call sequence.

·Realize the mapping and data flow conversion between layers, such as the calculation and feedback of D→I, I→K→ KW, W→P. For example, in our example, when the K-layer rule triggers Append(CurrentPath), the engine needs to write the data back to the CurrentPath structure of the layer I. When ValidMoves is called at layer K, the engine calculates the result to layer I, etc.

·Deal with recursion and rule reasoning. For recursive rules like Backtrack, the engine needs to maintain the call stack and do backtracking. This is similar to the backtracking mechanism of a logic programming engine, and needs to be designed to prevent stack overflows or infinite loops.

·Choose how to execute it: It can be interpreted (sentence by sentence iteration of the AST run) or compiled into intermediate code (IR) and then executed. For flexibility, K-layer rules may be executed in an interpretive manner plus a rule engine (e.g., Drools, Jess) or a custom logical reasoning framework. Technology selection should consider a combination of logical programming (Prolog style) and functional/declarative paradigm to achieve hierarchical reasoning. It is also important to support asynchronous execution and callbacks to meet the needs of complex semantic interactions (such as multi-agent parallelism).

  Runtime environment and output: The runtime is responsible for maintaining the state during program execution and outputting the results. It needs to maintain: the current data status of layer D and I (such as the updated matrix, the current value of CurrentPath, etc.), which rules are activated in layer K, and the policy parameters of layer W. Also collect auxiliary information such as debug logs and direction logs (corresponding to the DirectionLog in our example). In terms of output, the execution results (such as found paths, logs) and exceptions and semantic error reports generated during the run should be provided at the end of the run. In addition, the runtime also needs to provide an external API interface for interactive system calls and debugging. For example, the debugger can query the content of the current path at runtime, or the external system can pass in the new MazeData through the API to change the maze and then rerun. 

Second, the design of key modules

Combined with the above process, the key modules of the parser are:

1.Lexer: As mentioned above, it recognizes lexical units and outputs token streams. Realize the rapid generation of existing tools such as ANTLR. Because there are fewer DIKWP syntax custom symbols, the lexical part should be more straightforward. 

2.Parser: Build AST. Consider writing a language grammar using ANTLR, etc., and then generate Parser code. The syntax of DIKWP should be able to express the hierarchical structure, which can be enforced by syntax rules: for example, the program syntax is the order of P_block D_block I_block K_block W_block Exec_block, and each block has its own sub-rules. You also need to support nested expressions such as Exists and Filter. This part of the work is similar to designing a syntax that combines logical and functional languages, which is not simple, but there are examples to follow (such as Prolog, Haskell and other syntax elements can be used as references). 

3.Semantic Analysis and Type Inspection: This module needs to be well designed. Because DIKWP is strongly semantic-oriented, you need to check whether cross-layer references are legitimate. For example, the path referenced in the P layer must be defined by the I or D layer; The MazeData.Grid at layer I must have a definition at layer D, etc. This is similar to multilingual integration, requiring a symbol table structure to store the symbols available at each layer and to examine the context. In terms of types, each variable and function must have a type, for example, MazeData.Grid is a Matrix, and ValidMoves returns a collection. This can be done by leveraging existing static type system frameworks (e.g. through strongly typed languages such as OCaml/Haskell). Semantic dependency checking is special, and the parser needs to have built-in DIKWP hierarchical rules, which are not difficult to enumerate (the constraints mentioned earlier). 

4.Execution Engine: This is the most challenging part. We may need to customize a logical reasoning engine to execute K-layer declarations; At the same time, a functional runtime is used to execute logic such as W-layer functions and I-layer filtering. One idea is to convert different layers of code into pieces of code in different existing languages and then combine them: for example, convert K-layer rules into Prolog subroutines, W/I layer functions into Python or Java methods, and D-layer data with constant definitions everywhere. Then the whole EXEC schedules the parts. It can also be converted to a single intermediate representation to support unified implementation. A rules engine such as Drools can be considered for the K-layer because it supports rule-based inference and backtracking. For example, whenever ValidMoves is required at layer K, the layer I function is called, and the engine maintains the stack itself when backtrack recursion is executed. This execution mechanism can be modeled after Prolog's deep search strategy, but it needs to be combined with process calls. 

5.Runtime and Output: Implement a container that manages the state of the program. This is actually similar to a normal virtual machine, such as the JVM's runtime objects. We may need a Context object that contains the current values of all D/I variables, and the execution stack corresponding to the Context. Each time you enter the recursion, the clone or push context changes, and the backtrack pops. Direction log collection can be output to an external log at the same time as each Append DirectionLog. Exception handling monitors the situation of no solution and type error, and throws an exception to the upper layer. It can also support interaction: for example, in debugging mode, you can manually modify the value of a certain layer to see how the AI behavior changes. The runtime API allows you to pause execution, check the status of each layer, etc., to facilitate understanding of the internal processes of AI. This is especially valuable for artificial consciousness systems, as developers and ethics reviewers want to peek into the "mind" of AI. 

3. Sample process of language parsing

Combined with our maze example, the analysis execution process can look like this:

1.Lexer recognizes words such as P, Intent, "Find Ingress-to-Egress Path",MazeData, etc., and outputs a sequence of tokens. 

2.Parser generates AST according to the syntax: there are P, D, I, K, W, EXEC sub-nodes under the root node, and each of them expands the content subtree (such as the intent definition under the P node, and the Goal assertion subtree).

3.Semantic analysis check: Make sure that the Path in Goal refers to the CurrentPath of Layer I, and the ValidMoves in Exists refers to the functions defined in Layer I. The Recursive Backtrack returns that the bool matches the Result. After passing, annotate the AST with type information such as Backtrack: Coordinate -> Bool.

4.Execution Engine：

·Initialize the D-layer structure such as MazeData.

·Determine the solution task based on the P-layer Goal (possibly set as a constraint for the engine to check for success conditions).

·Call Backtrack(StartPos) from EXEC. The engine creates a stack frame to record the current Pos=StartPos, and calls the rule to enter the K layer.

·Execute K-layer IF: Check MazeData.Grid[Pos]== 'E' No, if not, go ELSE.

·ELSE part: Set Visited[Pos]=True, CurrentPath append Pos. The Exists Dir in ValidMoves (Pos) condition is then processed. The engine calculates ValidMoves: This triggers the Layer I Filter function, assisted by the W layer Move, and WithinBounds to get the set of directions. The engine selects the first Dir, executes Append(DirectionLog, Dir), and then calls Backtrack(Move(Pos,Dir)) recursively. At this point the new frame is pressed, and the process is repeated...

·If a recursion encounters the endpoint E, the rule returns a TRUE straight bounce. When the engine returns along the stack, it constructs a return value (Result=TRUE, CurrentPath filled, DirectionLog somewhat).

·OUTPUT: The engine outputs the result to the runtime based on the Result and the CurrentPath of the current context.

·The runtime displays or passes the result to the caller and cleans up the state.

 Technical implementation suggestions: as mentioned earlier, such as ANTLR generates parsers, semantic analysis is implemented functionally, the execution engine is based on rule machines/logical languages, the runtime is multi-threaded... These are optimizations that can be made for performance and reliability.

Fourth, technical implementation suggestions

To summarize some implementation points:

·Parser generation: You can use tools such as ANTLR and Bison to quickly construct lexical + grammatical analysis. Then handwriting semantic checks. 

·Language selection: It is convenient to implement a semantic analysis layer in a functional language (e.g., Haskell/OCaml) because it is good at dealing with recursive and type systems. The actual parser can be implemented by Java/Scala as the main framework, as engines such as Drools are mostly on the JVM. 

·Execution engine: You can embed the Prolog engine to handle K-layer rule inference, or use a Z3-like solver to handle Goal constraint validation. Custom stack machines are also available. The Drools/Jess rule engine is used in the K layer, which can be used for forward inference and also supports backtracking to a certain extent. 

·Concurrency: The runtime must support hierarchical data state synchronization and multi-threaded backtracking calculations. If you want to support parallel search or multi-agent in the future, you need to consider thread safety, and you may have separate locks on each layer of data. 

5. Summary

This parser framework strives to be clear, semantic, extensible, and easy to debug:

·The module is clear: the responsibilities of each layer of the parser correspond to the DIKWP five-layer model, and the responsibilities are clear. Lexer/Parser/Analyzer/Engine/Runtime step by step, each performing its own role, without confusion. 

·Semantic enrichment: AST and the engine fully express the cognitive semantic features of the DIKWP programming paradigm. Unlike traditional compilers that lose semantics, here we leave the hierarchy to the runtime for easy interpretation and monitoring. 

·Scalability: More cognitive mechanisms, rule systems, and inference strategies can be introduced into the architecture. For example, if you want to add a layer (such as the emotion layer E?) or a new rule type in the future, there are corresponding modules to deal with it, and it will not affect the other parts too much. 

·Easy to debug: A clear hierarchical structure makes it easy to locate semantic errors and debug. Developers can individually test whether the Layer I functions are correct, or whether the Layer K rules cover all cases, which is much easier than debugging a mess of monolithic code. 

Through this framework design, we have a preliminary vision of the engineering path to implement a DIKWP language. Of course, it took a lot of work to polish the details, but at least it proved to be feasible. Professor Duan Yucong has also pointed out that the DIKWP model can be implemented into a new generation of semantic-driven programming language, and its interpreter itself supports a five-layer structure of data and inference flow – our design confirms this.

7.6 Solving simulations based on the DIKWP programming language

In this section, we will take a simplified maze as an example to simulate the execution steps of the maze backtracking algorithm designed in the DIKWP programming language in the interpreter, focusing on the dynamic interaction of the five layers of DIKWP, the data flow between the layers, and the cognitive semantic evolution.

Example maze setup (simplified):

·Starting Point S: (0,0)

·Exit E: (0,2)

·Labyrinth matrix (coordinate annotation) :(0,0) S (0,1) 0 (0,2) E(1,0) 1 (1,1) 0 (1,2) 1           

(2 rows and 3 columns maze, 1 represents the wall).

Detailed explanation of the execution steps (the key process of the interpreter to execute the DIKWP program):

Step number	DIKWP layer	Activity content and data status	illustrate
1	P (Intent)	Set a goal: find the path from (0,0) to (0,2)	The "why" that initiates the execution of the algorithm. The parser reads the P-layer intent and goal to clarify the target conditions.
2	D (Data)	Initialize the maze grid and access matrix, entrance coordinates = (0,0)	The static environment and state that the program depends on. MazeData is populated with Grid=[[S,0,E],[1,0,1]], etc., and the Visited matrix is all False.
3	I (Information)	The current path is empty, and the direction log is empty	Dynamic information has not yet been generated. CurrentPath=[], DirectionLog=[] initial state.
4	W (Wisdom)	Set the order of movement direction: up, down, left, right	Form a search strategy. The DirectionSet is defined and the Move and WithinBounds functions are ready.
5	K (Knowledge)	Start executing the Backtrack(0,0) function	Enter the core recursion rules. The parser calls the K-layer Backtrack (StartPos) when executing EXEC.
6	D→I	Mark (0,0) as visited, path join(0,0)	The status of the data is updated and information is generated. Visited[0,0]=True，CurrentPath=[(0,0)]。 This is the change in the D and I layers caused by the K-layer rule.
7	I→W	Calculate the effective movement of (0,0): Move to (0,1) (right)	Information derivation is based on the wisdom layer strategy. ValidMoves(0,0) checks the neighbors via the W layer Moves: upper out bounds, lower wall, left none, right 0 and no access→ return [RIGHT].
8	I→K	Direction "right" joins the direction log, call Backtrack(0,1)	Information-driven knowledge layer rule enforcement. The parser selects the RIGHT direction, Append(DirectionLog,"RIGHT(0,1)"), and then recursively Backtrack((0,1)).
9	K→D	Mark (0,1) as visited, path join(0,1)	Data status updates in recursion. Visited[0,1]=True, CurrentPath=[(0,0),(0,1)]。
10	D→I	Judge (0,1) non-exit and continue to calculate the effective move	The data status is changed to information judgment. maze[0,1]!=E,ValidMoves(0,1) -> Check neighbors: right(0,2) is E, not visited, valid; Below(1,1) is 0, not accessed, valid; RETURN TO [RIGHT, DOWN].
11	I→W	(0,1) Effective move is (0,2) (right)	The search strategy leads to the next step. In the order of DirectionSet, the upper left out of bounds or walls, and the right is valid first; While Down also works, the order is at the back of the right.
12	I→K	Direction "right" to join the log, call Backtrack(0,2)	Recursively deep. Add "RIGHT(0,2)" and "Backtrack((0,2)" to DirectionLog.