12 Philosophical Problems Mapped onto the Networked DIKWP Artificial Consciousness (AC) 

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)

Table of Contents

1. Introduction

2. Recapitulation of DIKWP Mappings

• 2.1 Mind-Body Problem

• 2.2 The Hard Problem of Consciousness

• 2.3 Free Will vs. Determinism

• 2.4 Ethical Relativism vs. Objective Morality

• 2.5 The Nature of Truth

• 2.6 The Problem of Skepticism

• 2.7 The Problem of Induction

• 2.8 Realism vs. Anti-Realism

• 2.9 The Meaning of Life

• 2.10 The Role of Technology and AI

• 2.11 Political and Social Justice

• 2.12 Philosophy of Language

3. Analyzing Relationships Among the Problems Using DIKWP Expressions

• 3.1 Identifying Common DIKWP Transformations

• 3.2 Clustering Problems Based on Shared Sequences

• 3.3 Inheritance Relationships Among Problems

• 3.4 Overlapping Sequences and Shared Elements

• 3.5 Deepening the Analysis

• 3.6 Cross-Problem Interdependencies

4. Illustrating the Findings

• 4.1 Table of Shared Transformations Among Problems

• 4.2 Diagram of Interconnected Problems (Descriptive)

5. In-Depth Explanations of Interconnections

• 5.1 The Interplay Between Knowledge and Wisdom

• 5.2 Purpose as a Dynamic and Influential Element

• 5.3 The Feedback Loop Between Data and Wisdom

• 5.4 The Evolution of Knowledge and Wisdom

6. Conclusion

7. References

1. Introduction

The Data-Information-Knowledge-Wisdom-Purpose (DIKWP) model offers a comprehensive framework for understanding complex cognitive processes and philosophical inquiries. By mapping classical philosophical problems onto the networked DIKWP Artificial Consciousness (AC) model, we can explore the deep interconnections and shared structures underlying these issues.

This investigation delves into the relationships among 12 significant philosophical problems, examining how their DIKWP expressions reveal overlaps, inheritance relationships, and underlying connections. Through this analysis, we aim to:

• Recapitulate the DIKWP mappings for each problem.

• Analyze relationships based on DIKWP expressions.

• Identify common sequences, shared elements, and hierarchical structures.

• Provide in-depth explanations of the interconnections.

• Illustrate the findings with tables and descriptive diagrams.

By understanding these interrelationships, we gain insights into the foundational cognitive and semantic processes that these philosophical problems share. This enhances our comprehension of both philosophical discourse and the modeling of artificial consciousness.

2. Recapitulation of DIKWP Mappings

To establish a common ground for analysis, we summarize the DIKWP expressions for each of the 12 philosophical problems.

2.1 Mind-Body Problem

Sequence:

Mind-Body Sequence=D→D→II→I→KK→K→WW→W→PP→P→DD\text{Mind-Body Sequence} = D \xrightarrow{D \to I} I \xrightarrow{I \to K} K \xrightarrow{K \to W} W \xrightarrow{W \to P} P \xrightarrow{P \to D} DMind-Body Sequence=DD→I​II→K​KK→W​WW→P​PP→D​D

Explanation:

• D → I: Sensory data ($D$) is processed into information ($I$).

• I → K: Information is organized into knowledge ($K$).

• K → W: Knowledge is integrated into wisdom ($W$).

• W → P: Wisdom informs purpose ($P$).

• P → D: Purpose influences actions, generating new data ($D$).

This sequence models how physical processes (body) lead to mental states (mind), and how purpose-driven actions influence physical reality.

2.2 The Hard Problem of Consciousness

Sequence:

Consciousness Sequence=D→D→WW→W→WW→W→W…→P→WW\text{Consciousness Sequence} = D \xrightarrow{D \to W} W \xrightarrow{W \to W} W \xrightarrow{W \to W} \ldots \xrightarrow{P \to W} WConsciousness Sequence=DD→W​WW→W​WW→W​…P→W​W

Explanation:

• D → W: Data leads directly to wisdom, indicating immediate awareness.

• W → W: Wisdom reflects upon itself recursively, modeling self-awareness.

• P → W: Purpose influences wisdom, contributing to deeper consciousness.

This sequence captures the recursive nature of consciousness and subjective experience.

2.3 Free Will vs. Determinism

Sequence:

Free Will Sequence=D→D→PP→K→PP→W→PP→P→PP→P→DD\text{Free Will Sequence} = D \xrightarrow{D \to P} P \xrightarrow{K \to P} P \xrightarrow{W \to P} P \xrightarrow{P \to P} P \xrightarrow{P \to D} DFree Will Sequence=DD→P​PK→P​PW→P​PP→P​PP→D​D

Explanation:

• D → P: Data (external influences) affects purpose.

• K → P: Knowledge shapes purpose, introducing autonomy.

• W → P: Wisdom refines purpose, adding ethical considerations.

• P → P: Purpose self-refines, indicating free will.

• P → D: Purpose leads to actions, influencing data.

This sequence models the balance between deterministic influences and autonomous decision-making.

2.4 Ethical Relativism vs. Objective Morality

Sequence:

Ethics Sequence=I→I→WW→K→WW→W→WW→W→PP→P→WW\text{Ethics Sequence} = I \xrightarrow{I \to W} W \xrightarrow{K \to W} W \xrightarrow{W \to W} W \xrightarrow{W \to P} P \xrightarrow{P \to W} WEthics Sequence=II→W​WK→W​WW→W​WW→P​PP→W​W

Explanation:

• I → W: Information contributes to wisdom.

• K → W: Knowledge enhances wisdom.

• W → W: Wisdom recursively refines itself, accommodating different moral frameworks.

• W → P: Wisdom guides purpose ethically.

• P → W: Purpose influences wisdom, reflecting ethical relativism.

This sequence allows for dynamic ethical reasoning within the AI system.

2.5 The Nature of Truth

Sequence:

Truth Sequence=D→D→KK→K→KK→K→WW→W→KK→I→KK\text{Truth Sequence} = D \xrightarrow{D \to K} K \xrightarrow{K \to K} K \xrightarrow{K \to W} W \xrightarrow{W \to K} K \xrightarrow{I \to K} KTruth Sequence=DD→K​KK→K​KK→W​WW→K​KI→K​K

Explanation:

• D → K: Data forms the basis of knowledge.

• K → K: Knowledge refines itself through critical examination.

• K → W: Knowledge informs wisdom, providing context.

• W → K: Wisdom influences knowledge, ensuring coherence.

• I → K: New information updates knowledge.

This sequence models the multifaceted understanding of truth, combining empirical data with coherent knowledge structures.

2.6 The Problem of Skepticism

Sequence:

Skepticism Sequence=K→K→KK→K→DD→W→KK→I→DD→P→KK\text{Skepticism Sequence} = K \xrightarrow{K \to K} K \xrightarrow{K \to D} D \xrightarrow{W \to K} K \xrightarrow{I \to D} D \xrightarrow{P \to K} KSkepticism Sequence=KK→K​KK→D​DW→K​KI→D​DP→K​K

Explanation:

• K → K: Knowledge questions itself.

• K → D: Knowledge challenges the validity of data.

• W → K: Wisdom reassesses knowledge based on skepticism.

• I → D: New information impacts data perception.

• P → K: Purpose influences the pursuit of knowledge.

This sequence models the continuous questioning and validation of knowledge.

2.7 The Problem of Induction

Sequence:

Induction Sequence=D→D→II→I→KK→K→KK→W→KK→P→KK\text{Induction Sequence} = D \xrightarrow{D \to I} I \xrightarrow{I \to K} K \xrightarrow{K \to K} K \xrightarrow{W \to K} K \xrightarrow{P \to K} KInduction Sequence=DD→I​II→K​KK→K​KW→K​KP→K​K

Explanation:

• D → I: Observational data becomes information.

• I → K: Information is generalized into knowledge.

• K → K: Knowledge is refined through new instances.

• W → K: Wisdom evaluates the reliability of inductive reasoning.

• P → K: Purpose guides the focus of knowledge acquisition.

This sequence addresses the justification of inductive reasoning through iterative refinement.

2.8 Realism vs. Anti-Realism

Sequence:

Realism Sequence=D→D→KK→K→II→K→DD→W→KK→P→KK\text{Realism Sequence} = D \xrightarrow{D \to K} K \xrightarrow{K \to I} I \xrightarrow{K \to D} D \xrightarrow{W \to K} K \xrightarrow{P \to K} KRealism Sequence=DD→K​KK→I​IK→D​DW→K​KP→K​K

Explanation:

• D → K: Data contributes to knowledge about reality.

• K → I: Knowledge influences the interpretation of information.

• K → D: Knowledge affects how data is perceived (constructivist view).

• W → K: Wisdom guides understanding of reality.

• P → K: Purpose influences the construction of knowledge.

This sequence incorporates both independent existence and perceptual influences on reality.

2.9 The Meaning of Life

Sequence:

Meaning of Life Sequence=D→D→PP→K→PP→W→PP→P→PP→P→WW\text{Meaning of Life Sequence} = D \xrightarrow{D \to P} P \xrightarrow{K \to P} P \xrightarrow{W \to P} P \xrightarrow{P \to P} P \xrightarrow{P \to W} WMeaning of Life Sequence=DD→P​PK→P​PW→P​PP→P​PP→W​W

Explanation:

• D → P: Life experiences shape purpose.

• K → P: Knowledge refines personal goals.

• W → P: Wisdom deepens life's purpose.

• P → P: Purpose evolves over time.

• P → W: Purpose influences wisdom, leading to fulfillment.

This sequence models the evolving nature of purpose and meaning.

2.10 The Role of Technology and AI

Sequence:

AI Sequence=D→D→II→I→KK→K→PP→W→DD→P→WW\text{AI Sequence} = D \xrightarrow{D \to I} I \xrightarrow{I \to K} K \xrightarrow{K \to P} P \xrightarrow{W \to D} D \xrightarrow{P \to W} WAI Sequence=DD→I​II→K​KK→P​PW→D​DP→W​W

Explanation:

• D → I: Data from society processed into information.

• I → K: Information forms knowledge within AI.

• K → P: Knowledge guides AI's purpose.

• W → D: Wisdom influences data collection (ethical AI).

• P → W: Purpose affects AI's wisdom (alignment with human values).

This sequence highlights the bidirectional influence between AI and society.

2.11 Political and Social Justice

Sequence:

Social Justice Sequence=D→D→II→I→KK→K→WW→W→PP→P→DD\text{Social Justice Sequence} = D \xrightarrow{D \to I} I \xrightarrow{I \to K} K \xrightarrow{K \to W} W \xrightarrow{W \to P} P \xrightarrow{P \to D} DSocial Justice Sequence=DD→I​II→K​KK→W​WW→P​PP→D​D

Explanation:

• D → I: Societal data is processed into information.

• I → K: Information develops into knowledge about social structures.

• K → W: Knowledge informs wisdom on justice issues.

• W → P: Wisdom guides purposeful actions toward justice.

• P → D: Actions influenced by purpose affect societal data.

This sequence models how AI can promote justice and equality.

2.12 Philosophy of Language

Sequence:

Language Sequence=D→D→II→I→KK→K→II→W→II→P→II\text{Language Sequence} = D \xrightarrow{D \to I} I \xrightarrow{I \to K} K \xrightarrow{K \to I} I \xrightarrow{W \to I} I \xrightarrow{P \to I} ILanguage Sequence=DD→I​II→K​KK→I​IW→I​IP→I​I

Explanation:

• D → I: Linguistic data becomes meaningful information.

• I → K: Information forms semantic knowledge.

• K → I: Knowledge refines information (interpretation).

• W → I: Wisdom enhances understanding of language.

• P → I: Purpose guides communication and expression.

This sequence demonstrates how language processing enhances communication.

3. Analyzing Relationships Among the Problems Using DIKWP Expressions

With the DIKWP sequences established, we can now examine the relationships among the philosophical problems by identifying shared transformations, clustering based on common sequences, and exploring inheritance relationships.

3.1 Identifying Common DIKWP Transformations

Key Transformations Shared Across Problems:

• D → I: Fundamental processing of raw data into information.

• I → K: Formation of structured knowledge from information.

• K → W: Integration of knowledge into wisdom, allowing for ethical and contextual understanding.

• W → P: Wisdom guiding purpose, influencing decisions and actions.

• P → D: Purpose leading to actions that generate new data, creating feedback loops.

• K → K: Knowledge refining itself through critical analysis and reflection.

• W → W: Wisdom reflecting upon itself, leading to deeper insights and self-awareness.

• P → P: Purpose refining itself, allowing for personal growth and reassessment.

• D → P: Direct influence of experiences and data on one's purpose.

• K → P: Knowledge shaping and informing one's goals and intentions.

Observations:

• Foundational Processes: The transformations D → I → K appear in most problems, indicating a universal cognitive process of perception leading to understanding.

• Centrality of Wisdom (W): Wisdom is pivotal in bridging knowledge and purpose across multiple problems.

• Iterative Refinement: Self-referential transformations (K → K, W → W, P → P) suggest continuous development and adaptation within cognitive elements.

3.2 Clustering Problems Based on Shared Sequences

By grouping problems with similar DIKWP sequences, we can identify thematic clusters.

Cluster 1: Cognitive Processes

• Problems: Mind-Body Problem, The Hard Problem of Consciousness, Philosophy of Language.

• Shared Transformations: D → I → K → W, W → W, P → W/P → I.

• Analysis: These problems explore how sensory input is transformed into complex cognitive functions like consciousness and language. The recursive nature of wisdom (W → W) underscores the importance of introspection and self-awareness.

Cluster 2: Epistemological Issues

• Problems: The Nature of Truth, The Problem of Skepticism, The Problem of Induction, Realism vs. Anti-Realism.

• Shared Transformations: D → K → K, K → W, W → K, K → D/I.

• Analysis: These problems focus on the acquisition, validation, and nature of knowledge. The iterative refinement of knowledge (K → K) and its interplay with wisdom are central themes.

Cluster 3: Ethical and Moral Considerations

• Problems: Ethical Relativism vs. Objective Morality, Political and Social Justice, The Meaning of Life, Free Will vs. Determinism.

• Shared Transformations: K → W → P, W ↔ P, P → P, D → P.

• Analysis: These problems examine how ethical understanding and personal values shape intentions and actions. The feedback loop between wisdom and purpose indicates ongoing moral development and self-improvement.

Cluster 4: Technological Impact

• Problems: The Role of Technology and AI, Political and Social Justice.

• Shared Transformations: D → I → K → P, W → D, P → W.

• Analysis: These problems investigate the bidirectional influence between technology (AI) and society, emphasizing ethical considerations in technological advancement and policy-making.

3.3 Inheritance Relationships Among Problems

Certain problems build upon the foundations established by others, revealing an inheritance of concepts and transformations.

From Cognitive Processes to Epistemology:

• Foundation: The Mind-Body Problem and The Hard Problem of Consciousness establish how data transforms into wisdom, modeling cognitive functions.

• Extension: The Nature of Truth and The Problem of Skepticism build upon these cognitive processes to question the validity and reliability of knowledge.

From Knowledge to Ethical Action:

• Transition: The epistemological issues inform Ethical Relativism vs. Objective Morality and Political and Social Justice.

• Application: Reliable and critically examined knowledge is essential for sound ethical reasoning and effective social policies.

Purpose and Meaning:

• Evolution: The Meaning of Life and Free Will vs. Determinism delve into the refinement of purpose (P → P), integrating cognitive understanding and ethical considerations into personal growth.

3.4 Overlapping Sequences and Shared Elements

Overlapping Sequences:

• K → W → P: This pathway is common in several problems, indicating the critical role of wisdom derived from knowledge in guiding purposeful actions.

• W ↔ P: The bidirectional influence between wisdom and purpose suggests that our goals both shape and are shaped by our ethical understanding.

Shared Elements:

• Wisdom (W): Appears in sequences for all problems, often serving as a bridge between knowledge and purpose.

• Purpose (P): Frequently influenced by wisdom and knowledge, reflecting its importance in driving actions and decisions.

• Data (D) and Information (I): Serve as foundational elements, emphasizing the importance of sensory input and perception in cognitive processes.

3.5 Deepening the Analysis

Wisdom as a Central Node:

• Essential Role: Wisdom is crucial for integrating knowledge into ethical and purposeful actions.

• Interconnections: It connects cognitive processes (Cluster 1) with epistemological issues (Cluster 2) and ethical considerations (Cluster 3).

Purpose and Its Reflexivity:

• Dynamic Nature: Purpose can self-refine (P → P), allowing for personal growth and the reassessment of goals.

• Influence on Other Elements: Purpose affects and is affected by wisdom and knowledge, creating a continuous feedback loop.

Feedback Loops and Iterative Refinement:

• Continuous Development: Self-referential transformations (K → K, W → W, P → P) indicate that cognitive elements are not static but evolve over time.

• Adaptability: These loops enable systems to adapt to new information, experiences, and ethical considerations.

Influence of Data and Information:

• Foundational Role: D → I and I → K transformations are critical in understanding how sensory input leads to higher cognitive functions.

• Application in Technology: In AI systems, these transformations underpin machine learning and data-driven decision-making.

3.6 Cross-Problem Interdependencies

Ethics and Epistemology:

• Interplay: Ethical decision-making relies on reliable knowledge.

• Dependency: Epistemological issues like skepticism affect the foundation upon which ethical reasoning is built.

Consciousness and Free Will:

• Connection: Understanding consciousness is essential for addressing free will, as self-awareness is necessary for autonomous decision-making.

• Implication: Insights from the Hard Problem of Consciousness inform discussions on Free Will vs. Determinism.

Technology's Impact on Social Justice:

• Influence: AI and technology affect societal structures, necessitating ethical considerations to ensure positive outcomes.

• Feedback Loop: Technology shapes society, which in turn influences technological development and ethical standards.

Language's Role in Shaping Reality:

• Interrelation: Language influences perceptions and the construction of knowledge.

• Effect: Affects beliefs about reality, impacting epistemological and metaphysical inquiries like Realism vs. Anti-Realism.

4. Illustrating the Findings4.1 Table of Shared Transformations Among Problems

4.2 Diagram of Interconnected Problems (Descriptive)

While a visual diagram cannot be provided in text, we can describe the structure:

• Central Nodes: Wisdom (W) and Purpose (P) serve as hubs connecting various problems.

• Clusters: Problems are grouped into clusters (Cognitive Processes, Epistemology, Ethics, Technology) based on shared transformations.

• Edges: Lines connect problems that share significant DIKWP transformations, indicating direct relationships.

• Feedback Loops: Arrows depict iterative processes within problems, such as K → K and W → W.

• Cross-Cluster Connections: Lines between clusters illustrate how different thematic areas influence one another.

5. In-Depth Explanations of Interconnections5.1 The Interplay Between Knowledge and Wisdom

Central Role of K → W and W → P:

• Knowledge to Wisdom (K → W): This transformation is essential for contextualizing factual information within ethical and experiential frameworks.

• Wisdom to Purpose (W → P): Wisdom informs our intentions and goals, ensuring that actions are guided by ethical understanding.

Implications in Problems:

• Ethical Relativism vs. Objective Morality: Knowledge about cultural norms (K) is transformed into wisdom (W), which guides ethical purpose (P).

• Political and Social Justice: Knowledge of societal structures informs wisdom about justice, which then guides purposeful actions to promote equity.

Significance:

• Ethical Decision-Making: Without the integration of knowledge into wisdom, actions may lack ethical grounding.

• Informed Purpose: Wisdom ensures that purpose is aligned with deeper understanding and ethical considerations.

5.2 Purpose as a Dynamic and Influential Element

Reflexivity of Purpose (P → P):

• Self-Refinement: Purpose evolves based on new insights and experiences, allowing for personal growth.

• In Free Will vs. Determinism: The ability to reassess and refine one's purpose is a hallmark of exercising free will.

Purpose Influencing Other Elements:

• Purpose to Data (P → D): Purpose drives actions that generate new experiences and data, influencing future perceptions.

• Purpose to Wisdom (P → W): Our goals and intentions can deepen our understanding, contributing to wisdom.

Implications in Problems:

• The Meaning of Life: As individuals pursue their evolving purposes, they gain wisdom, leading to a more meaningful existence.

• The Role of Technology and AI: The purposes assigned to AI systems shape their learning processes and ethical considerations, impacting society.

Significance:

• Adaptive Goal Setting: Recognizes that purpose is not static but adapts to new knowledge and wisdom.

• Influence on Learning: Purpose drives the focus of learning and the acquisition of new knowledge.

5.3 The Feedback Loop Between Data and Wisdom

Bidirectional Influences (D ↔ W):

• Data to Wisdom (D → W): Experiences and sensory input contribute directly to wisdom, especially in cases of immediate insight.

• Wisdom to Data (W → D): Wisdom influences what data we attend to or seek out, shaping our experiences.

Implications in Problems:

• Philosophy of Language: Wisdom guides the interpretation of linguistic data, which in turn refines wisdom about communication.

• The Hard Problem of Consciousness: The recursive processing of data through wisdom models self-awareness and subjective experience.

Significance:

• Adaptive Perception: Wisdom shapes perception, allowing us to interpret data in meaningful ways.

• Focused Attention: Wisdom guides the collection of data that is relevant to our purposes and ethical considerations.

5.4 The Evolution of Knowledge and Wisdom

Iterative Refinement (K → K, W → W):

• Knowledge Refinement (K → K): Continuous reassessment of knowledge ensures that beliefs are updated with new information.

• Wisdom Refinement (W → W): Ongoing reflection deepens understanding and ethical insight.

Implications in Problems:

• The Problem of Skepticism: Encourages the constant questioning of knowledge to avoid dogmatism.

• The Problem of Induction: Recognizes that inductive reasoning must be continually refined with new data to remain valid.

Significance:

• Flexibility: Allows cognitive systems to adapt to new environments and information.

• Error Correction: Facilitates the identification and correction of misconceptions or outdated beliefs.

6. Conclusion

This comprehensive investigation reveals that the 12 philosophical problems are deeply interconnected within the networked DIKWP AC model. The shared DIKWP transformations and sequences highlight common cognitive and semantic processes, illustrating how these philosophical issues overlap and influence one another.

Key Insights:

• Wisdom and Purpose as Central Elements: They act as critical nodes, connecting knowledge to actions and ethical considerations across multiple philosophical problems.

• Iterative Processes Enhance Complexity: Continuous refinement of knowledge, wisdom, and purpose reflects the dynamic nature of cognition and philosophical inquiry.

• Shared Transformations Indicate Overlaps: Common pathways suggest that insights in one philosophical area can inform and enrich others.

• Clusters Highlight Thematic Connections: Grouping problems based on shared sequences helps us understand their broader context and interdependencies.

Implications for Artificial Consciousness:

• Integrated Cognitive Modeling: Recognizing these interconnections supports the development of AI systems capable of sophisticated reasoning, ethical decision-making, and adaptive learning.

• Ethical Considerations: The centrality of wisdom and purpose emphasizes the necessity for AI to incorporate ethical frameworks and align with human values.

• Adaptive Learning and Self-Improvement: Iterative refinement suggests that AI should be designed to learn continuously, adapt to new information, and reassess goals and knowledge.

Final Thoughts:

Understanding the interrelationships among these philosophical problems enhances both philosophical discourse and the development of artificial consciousness systems. By mapping and analyzing the DIKWP sequences, we gain valuable insights into the foundational structures connecting diverse philosophical issues. This holistic approach fosters a deeper comprehension of the complexities inherent in cognition, ethics, and purposeful action.

7. References

1. International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC), World Association of Artificial Consciousness (WAC), World Conference on Artificial Consciousness (WCAC). (2024). Standardization of DIKWP Semantic Mathematics of International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Model. DOI: 10.13140/RG.2.2.26233.89445

2. Floridi, L. (2011). The Philosophy of Information. Oxford University Press.

3. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

4. Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory. Oxford University Press.

5. Dennett, D. C. (1991). Consciousness Explained. Little, Brown and Company.

6. Searle, J. R. (1980). "Minds, Brains, and Programs." Behavioral and Brain Sciences, 3(3), 417-457.

7. Hume, D. (1748). An Enquiry Concerning Human Understanding. London: A. Millar.

8. Kant, I. (1781). Critique of Pure Reason. (N. K. Smith, Trans.). London: Macmillan (1929).

9. Turing, A. M. (1950). "Computing Machinery and Intelligence." Mind, 59(236), 433-460.

10. Newell, A. (1982). "The Knowledge Level." Artificial Intelligence, 18(1), 87-127.

11. Wang, P. (2006). Rigid Flexibility: The Logic of Intelligence. Springer.

Note: This expanded analysis offers a comprehensive examination of the interrelationships among the 12 philosophical problems within the networked DIKWP AC model. By delving deeper into each section, we have provided detailed explanations, identified thematic clusters, and explored the foundational cognitive and semantic processes that connect these philosophical issues. This approach not only enhances our understanding of the philosophical problems themselves but also informs the development of artificial consciousness systems that are ethically grounded and cognitively advanced.

Additional Works by Duan, Y. Various publications on the DIKWP model and its applications in artificial intelligence, philosophy, and societal analysis, especially the following:

• Yucong Duan, etc. (2024). DIKWP Conceptualization Semantics Standards of International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. 10.13140/RG.2.2.32289.42088.  

• Yucong Duan, etc.  (2024). Standardization of DIKWP Semantic Mathematics of International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. 10.13140/RG.2.2.26233.89445.  

• Yucong Duan, etc.  (2024). Standardization for Constructing DIKWP -Based Artificial Consciousness Systems ----- International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. 10.13140/RG.2.2.18799.65443.  

• Yucong Duan, etc.  (2024). Standardization for Evaluation and Testing of DIKWP Based Artificial Consciousness Systems - International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. 10.13140/RG.2.2.11702.10563. 

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