Complexity Analysis of Artificial Consciousness Systems Based

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Complexity Analysis of Artificial Consciousness Systems Based

通用人工智能AGI测评DIKWP实验室

2025-10-30

Complexity Analysis of Artificial Consciousness Systems Based on DIKWPTransformation and Consciousness Relativity (Simplified Version)

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)

1. Introduction

Since artificial intelligence has entered the realm of cognition, semantics, and autonomous consciousness, traditional complexity theories have become increasingly unable to meet the measurement and optimization needs of complex systems. The DIKWP model proposed by Professor YucongDuannot only provides a solid structure for cognitive modeling, but also provides a new idea for "the complex nature of artificial consciousness systems". Focusing on cutting-edge concepts such as "partial convertibility of content", "semantic elastic network" and "subjective relative complexity", this paper proposes a future-oriented complexity analysis method with semantic space as the core, trying to break through the limitations of traditional engineering and the paradigm of arithmetic step summing.

2. Theoretical basis and research motivation

2.1 Challenges of the complexity of artificial consciousness systems

Current AI systems (whether large models, inference engines, or autonomous robots) are faced with a "interpretation and adaptation" problem: computing power and algorithm scale alone cannot guarantee that agents can truly understand their own behavior and interpret the semantic meaning of each step.

Artificial consciousness pursues "self-interpretation, self-understanding, and self-adaptation"—its complexity has gone beyond the input-output relationship and is embodied in the flow and transformation of multi-layer semantic space.

2.2 The innovative significance of the DIKWP model

DIKWP divides all the cognitive content of the system into five layers: data (D), information (I), knowledge (K), wisdom (W), and purpose (P).

The "partial convertibility" of DIKWP content breaks the rigid barriers between layers, so that the system can flexibly combine content and carry out self-organization and self-completion in the face of complex scenarios such as semantic faults, content ambiguity, and target drift.

2.3 Theory of Consciousness Understanding and Relativity of Consciousness

Consciousness comprehension theory: It is believed that the essence of consciousness is "self-growth of content and semantic self-consistency", that is, any self-explanatory and self-adaptive agent must have the ability to continuously evolve its own understanding system in its own semantic space.

Relativism of consciousness: The comprehension, complexity perception, and content transformation path of each agent are subjective and relativistic, and there is no absolutely unique semantic structure. Complexity must be measured in terms of a "principal-content network" relationship.

3. DIKWP Content Convertibility and Semantic Space Modeling

3.1 Definition and Types of Content Convertibility

Fully convertible: The entire content of one layer can be transferred to another layer losslessly, as in classic data feature engineering.

Partially convertible: Content overlaps and mappings, but may have information loss, ambiguity, or the need to add new content.

Non-convertible: There is no direct transformation path between content, and must be bridged through external knowledge, completion mechanisms, or multi-step mediation.

3.2 Ontological structure of Semantic Space

Node: A specific instance of DIKWP content (e.g., "visual characteristics of cats", "purpose of a table").

Edges: Transition paths between contents, with weights indicating semantic transformation difficulty, attrition, or comprehension effort

Subspace: Each layer (D, I, K, W, P) is a subnet of the semantic space, but there are multiple intersections between the subnets

Spatial evolution: content is constantly flowing, and the semantic space can be expanded, reconstructed, contracted, and split to form a semantic ecosystem

3.3 Networked Content Mapping

Many-to-many mapping: A set of D/I content can be projected as a set of K/W content (e.g., a set of perceptual patterns supporting a decision rule).

Ring and jump transformations: such as the self-circulation of D→I→K→W→P→D, and "cross-layer jumps" such as I→W, K→P, etc

Fuzzy transformation and content redundancy: The same content can be represented by multiple equivalent or approximate instances in different subspaces (reflecting the ambiguity and ambiguity in understanding theory).

4. The Theory of Conscious Understanding and the Role of Conscious Relativism in Complexity

4.1 Understand the dynamic nature of circulation

Complexity is no longer equated with the number of steps, but rather on the system's ability to achieve P-tier goals consistently within its own content network.

Each comprehension is an "optimal flow" of a content network—when the complexity is high, it requires a combination of multiple steps and paths to achieve the goal; Content "pass-through" purpose when complexity is low.

4.2 Subjectivity and Relative Complexity

Different subjects have their own content networks, and complexity is actually a function of "content coverage density", "network connectivity", and "interpretation path length" within the subject.

Agent A can easily generate layer P from layer I (simple understanding), while agent B can get stuck in layer K (complex understanding) – complexity relativism.

4.3 Introduction of "Understanding Resilience".

Semantic elasticity refers to the ability of a system to self-organize its content to self-heal, fill gaps, and ultimately achieve its purpose through self-organization of content.

Elasticity is essentially a product of "content redundancy + network multipath".

5. Semantic complexity metrics

5.1 Comparison of traditional and new complexity measures

index	Traditional methods	A new paradigm in semantic space
Step complexity	Count of algorithm steps/processes	The hop count and total length of the semantic flow path
Spatial complexity	Number of storage/nodes	The coverage volume and content redundancy of the semantic web
Understand complexity	Difficult to measure	Shortest self-explanatory path/maximum transform elasticity
Subjective complexity	Ignore subject differences	A function of the structure of the principal network and the perceived connectivity

5.2 Core Indicators of Semantic Complexity

a. Semantic Flux

The sum of all paths from input to purpose and their average length for all content in the system

High Turnover = High Complexity/High Flexibility

b. Comprehension Elasticity

Describes the ability of the system to recover/self-complete and achieve goals when content is disturbed (e.g., ambiguity, ambiguity, loss of content).

It is positively correlated with content redundancy and path diversity

c. Comprehension Gradient

The smaller the gradient from the input to the shortest semantic hops required for the target intent, the smoother the understanding

d. Subjective Complexity

Measured with the number of self-explanatory paths and density of the system.
Subject A and Subject B face the same problem, and their complexity assessments can be completely different

e. Semantic Coverage

The total amount of content that can be actively understood and covered by the current system

6. Semantic elasticity and system self-explanatory mechanisms

6.1 Elastic Networks and Conservation of "Semantic Energy".

Content flows in the network in the form of "semantic energy", and some of the loss can be filled by redundant paths

Elasticity is reflected in the existence and self-recovery ability of "multiple equivalent paths".

6.2 Self-explanatory and complexity self-regulation

Each content node has an "auto-explaining" subnet (all reachable paths from the node to layer P)

The system can dynamically monitor the smoothness and fault of the interpretation path, and automatically initiate "content reorganization", "knowledge completion" or "purpose reconstruction"

6.3 Complexity burst and entropy change

When there is a "content island" or a broken path in the network, the complexity increases dramatically, and the system needs to be reconstructed spontaneously

Similar to the "entropy increase" of a physical system, the system relies on external inputs or self-growing mechanisms to reduce complexity

7. Semantic Flow Network and Content Transformation Matrix

7.1 Network Modeling and Analysis

Node: Content instance (e.g., "Dog Barking Signature", "Room Seating Distribution").

Edge: The transform path, weighted by transformation difficulty or loss

Shortest Circuit Analysis: The shortest/optimal path to understanding content to intent

Network density: A high-density network indicates high elasticity and low complexity

7.2 Construction and application of transformation matrices

The 5x5 matrix M,M[i][j] represents the minimum comprehension cost of converting from layer i content to layer j content

Sparse matrix = rigid system, dense matrix = elastic system

Based on this, "content scheduling optimization" can be carried out, such as strengthening weak edges and adding redundant paths

7.3 Dynamic Evolution and Network Evolution

The system can automatically "grow" new content/paths based on the performance of historical tasks, evolving into a network with lower complexity and higher adaptability

Complexity = the external manifestation of the network's self-growth and self-regulation ability

8. Multi-agent collaboration and the complexity of social awareness networks

8.1 Multi-agent semantic synergy

Each subject has its own content network and complexity evaluation system

In inter-agent collaboration (such as AI swarms and intelligent robot swarms), the overall complexity of the system is not just a single sum, but a network superposition and mutual bridge

8.2 Collaborative Complexity Metrics

Shortest Collaborative Path: The shortest path for content transfer between the P layers of any two subjects

Collaborative elasticity: the degree of content complementarity and redundancy between subjects

Social complexity entropy: the density, coverage, and interoperability of the entire network

8.3 The "semantic energy flow" of social consciousness

In the collaborative scenario, the system can distribute and transform complex content among different subjects, and use distributed redundancy to reduce the overall complexity

The self-explanatory and self-organizing ability of "social consciousness" depends on the semantic elasticity and circulation efficiency of the whole network

9. Case Study: Adaptive Robots and Swarm Collaborative Intelligence

9.1 Anatomy of adaptive semantic complexity of monolithic robots

Scenario: Blurring target understanding and behavior generation

Input: "Look for a 'blue object with wheels'".

Layer D: Image pixel stream, laser point cloud, motion information

Layer I: A combination of features such as color, contour, and motion trajectory, and the content can be partially transformed into a target candidate set

K-layer: The concept of "wheel" in the knowledge base is clearly defined, but the "blue" distribution is vague, which needs to be judged by reasoning and the context of W-layer

Layer W: Adopt multi-strategy exploration, first detect the "motion + blue" target, and then verify the "wheel" characteristics

P-layer: The purpose is to "find the target object and get closer".

Analysis:

There is a fuzzy boundary in the I→K transition, and the W layer needs to introduce behavioral elasticity (multipath strategy attempt)

The gradient of understanding rises due to conceptual gaps and increases in complexity

If the robot has a self-learning mechanism, it can dynamically "grow" I-K bridge content, and the complexity will be reduced next time

9.2 Complexity of swarm robot collaboration

Multiple robots can share content interpretation (e.g., A focuses on color, B focuses on structure), and the system is most elastic when the P layer has the same goal

The shortest co-path in a collaborative network = the lower bound of complexity of task decomposition and synthesis

If the content network of a bot is broken, it can be bridged and completed by other bot content

10. Engineering implementation and tool system

10.1 Semantic Complexity Modeling Platform

The "content instance + convertible edge" network is automatically extracted and visualized

Real-time calculation of dynamic path analysis, understanding gradients, and elastic margins

Provides complex hotspot tracking, content fault alarm, and network self-growth scheduling APIs

10.2 DIKWP Content Forwarding Engine

Content Scheduling Optimizer Based on Content Circulation and Transformation Matrix

Support different levels of self-explanatory paths and dynamic elasticity adjustment

Seamless integration with multi-agent systems and social awareness networks

10.3 System self-interpretation and cognitive feedback

The system outputs "this round of complexity explanation": for example, "this P-layer target needs to be completed twice, and the understanding gradient is increased by 30%"

Support user intervention and external knowledge injection to achieve "collaborative understanding and co-construction"

11. Methodological Criticism and Future Prospects

11.1 Comparison and Criticism

Traditional complexity measures lack explanatory power and moderating ability in pure cognitive and semantic self-organization systems

Although this method improves the semantic transparency of the system, it depends on the completeness of content modeling and semantic network, and the actual engineering implementation still needs the support of deep automation tools

11.2 Future Trends

"Complexity economics" for content flow - AI systems actively "invest" in complementing flexibility and reducing the cost of understanding

"Social Complexity Engineering" under Multi-agent Collaboration: Using Content Networks to Optimize Group Consciousness, Social Cognition and System Evolution

Deep coupling of semantic complexity with AI ethics, controllability, and responsibility attribution

11.3 Philosophical Reflections

The complexity of the artificial consciousness system is not only a reflection of the physical world, but also a reflection of the depth of the subject's self-understanding

The evolution of truly strong AI will move towards a self-explanatory system that "actively reduces subjective complexity and improves the self-consistency of multiple content".

12. Summary and Enlightenment

The partial convertibility of DIKWP content and the analysis of semantic space complexity are indispensable theories and tools for future cognitive intelligence and artificial consciousness systems. It not only answers the question of "why AI can understand, how to explain itself, and how to adjust across layers", but also provides a new paradigm for engineering practice, intelligent optimization, and human-machine-society collaboration. In the future, the artificial consciousness system will go beyond computing power and scale, and move towards a new era of semantic flexibility, content self-consistency, and symbiosis of understanding.