The 3-No Problems Based on the DIKWP Network Model

段玉聪

人工智能评估的网络化DIKWP国际标准化委员会（DIKWP-SC）

世界人工意识CIC（WAC）

世界人工意识会议（WCAC）

（电子邮件：duanyucong@hotmail.com）

Abstract

Professor Yucong Duan’s concept of the 3-No Problems—Incomplete, Inconsistent, and Imprecise information—highlights a fundamental challenge in cognition and reasoning under the Open World Assumption (OWA). Solving these problems emphasizes an intention-driven approach, leveraging the semantic space to dynamically generate solutions that satisfy goals, without relying on the precise, complete, or consistent representation of individual components within the conceptual space. This report explores the 3-No Problems in-depth, with an analysis of their manifestations in the DIKWP network model, formalized transformations, and expanded applications in real-world scenarios.

1. Redefining the 3-No Problems and Their Semantic Nature

1.1 Problem Definitions

• Incomplete: Missing necessary information in the system's input or reasoning process, leading to gaps in understanding.

• Inconsistent: Contradictory information or knowledge that disrupts reasoning or decision-making.

• Imprecise: Ambiguity in input or reasoning outputs, resulting in unclear or uncertain semantic expressions.

1.2 Limitations of Traditional Methods

• Traditional methods are based on the Closed World Assumption (CWA), presuming that all relevant information is complete and consistent. The 3-No Problems challenge this assumption.

• Under OWA, systems often face incomplete, inconsistent, or imprecise inputs, which traditional linear cognitive models (e.g., DIKW) cannot effectively handle.

1.3 Core Insights from Professor Yucong Duan

• Intention-Driven Approach:

• Goal-Oriented Reasoning: The intention drives the goal-setting process, mapped within the semantic space.

• Goals exist within the semantic space without depending on precise or complete definitions in the conceptual space.

• Semantic Generation:

• The semantic space serves as the foundation for solving 3-No Problems by enabling multidimensional associations and fuzzy matching to create solutions.

2. Dynamic Features of the DIKWP Network Model

2.1 Definition of the Network Model

The DIKWP model is a dynamic network cognition model, comprising five key elements:

• D (Data): Basic entities representing "sameness."

• I (Information): Semantic relations among data, indicating "difference."

• K (Knowledge): Structured expression of information, embodying "completeness."

• W (Wisdom): Dynamic decision-making capability built upon knowledge.

• P (Purpose): The driving force guiding DIKWP transformations.

2.2 Transformation Mechanisms

The network model enables multidimensional, dynamic transformations:

• 5×5 Transformation Matrix: Each element can serve as input or output, resulting in 25 potential interaction pathways.

• Bidirectional Feedback: Outputs can influence inputs, creating a cognitive and reasoning feedback loop.

3. Manifestations of the 3-No Problems in the DIKWP Network Model

The 3-No Problems manifest differently across various DIKWP layers and their interaction pathways. The following provides an analysis of specific manifestations and solutions within the model:

3.1 Incomplete ProblemManifestations:

• Data Deficiency: Missing critical data points.

• Information Shortages: Incomplete associations among data.

• Knowledge Gaps: Missing necessary background knowledge.

Resolution Strategies:

1. Semantic Space Completion:

• Use existing data to generate missing portions via semantic associations.

• Example: Inferring global trends from local samples.

• Transformation Path: D → I → K

2. Knowledge-Based Data Estimation:

• Derive missing data from known knowledge bases.

• Example: Predicting climate changes using weather models.

• Transformation Path: K → D

3. Wisdom-Driven Decision Optimization:

• Focus on generating actionable decisions without requiring complete data.

• Transformation Path: W → P

3.2 Inconsistent ProblemManifestations:

• Data Conflicts: Contradictory results from different data sources.

• Information Contradictions: Conflicting descriptions of the same event.

• Knowledge Logical Conflicts: Contradictory reasoning rules.

Resolution Strategies:

1. Wisdom-Based Reconciliation:

• Resolve conflicts through weighting mechanisms or conflict elimination.

• Transformation Path: W → K

2. Intention-Driven Selection:

• Prioritize reliable information based on the goal defined in the purpose space.

• Transformation Path: P → I

3. Knowledge Logic Reconstruction:

• Reorganize logical relationships among knowledge to generate new reasoning pathways.

• Transformation Path: K → K

3.3 Imprecise ProblemManifestations:

• Ambiguous Data: Uncertain or noisy input data.

• Vague Information: Descriptions that are difficult to interpret.

• Unclear Decisions: Ambiguity in reasoning results.

Resolution Strategies:

1. Semantic Fuzzy Processing:

• Allow fuzzy reasoning to generate approximate solutions in the semantic space.

• Transformation Path: I → K → W

2. Goal-Oriented Refinement:

• Disambiguate fuzzy results through intention-guided processes.

• Transformation Path: P → W → I

3. Data Feedback Optimization:

• Refine data collection and processing based on initial fuzzy results.

• Transformation Path: W → D

4. Mathematical Formalization

4.1 Intention-Driven Transformation Function

• Target Function:T=fP(DIKWP)T = f_{P}(DIKWP)T=fP​(DIKWP)

• fPf_{P}fP​: Intention-driven target generation function.

• $DIKWP$: The current set of input elements.

4.2 Path Optimization

• Define pathway weight optimization:W(eij)=g(P,Rij)W(e_{ij}) = g(P, R_{ij})W(eij​)=g(P,Rij​)

• W(eij)W(e_{ij})W(eij​): Weight of the pathway from node DIKWPiDIKWP_iDIKWPi​ to DIKWPjDIKWP_jDIKWPj​.

• $P$: Priority in the purpose space.

• RijR_{ij}Rij​: Contextual relevance of the transformation rule.

5. Applications and Case Studies

5.1 Innovation Design

• Scenario: Designing a new product with incomplete market demands, inconsistent technical paths, and imprecise user feedback.

• Solution:

• Purpose space defines innovation goals.

• Semantic space generates potential designs.

• Knowledge optimizes design paths.

5.2 Smart Healthcare

• Scenario: Generating diagnostic solutions despite incomplete patient data, conflicting examination results, and vague symptom descriptions.

• Solution:

• Wisdom reconciles conflicting information.

• Intention guides knowledge retrieval from medical databases.

6. Conclusion and Future Outlook

The 3-No Problems identified by Professor Yucong Duan shift the cognitive paradigm from precision-focused approaches to intention-driven, semantic goal generation. By leveraging the dynamic transformation mechanisms of the DIKWP network model, these problems can be effectively addressed under the OWA. This novel approach provides a robust framework for applications in artificial intelligence, artificial consciousness, and knowledge management. Future work can further optimize mathematical representations and explore real-world implementations of the model.