Solving 3-No Problems Using the DIKWP 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）

This detailed expansion explores how the DIKWP model and Professor Yucong Duan's 3-No Problem-solving framework can be applied to address challenges of Incomplete, Inconsistent, and Imprecise data in complex systems. By leveraging intent-driven dynamic optimization, DIKWP provides an effective pathway to generate actionable solutions. Below is a more detailed breakdown of its practical application.

1. Scenario Description: Intelligent Medical Diagnosis System1.1 System Background

An intelligent medical diagnosis system aims to assist doctors in providing preliminary diagnostic suggestions by analyzing patient medical history, real-time monitoring data, and medical knowledge. The challenges include:

1. Incomplete Problem: Missing critical examination results in patient medical records.

2. Inconsistent Problem: Conflicting results from diagnostic tools (e.g., imaging and lab tests).

3. Imprecise Problem: Vague initial descriptions from doctors, such as "the symptoms might suggest mild infection."

1.2 System Goals

The system aims to provide preliminary diagnostic recommendations quickly, prioritize high-risk factors, and suggest relevant treatment options.

2. Solutions to 3-No Problems Using DIKWP2.1 Incomplete Problem

Problem Description:

• Missing critical examination results, such as blood test findings.

Solution Pathways:

1. Knowledge Compensating Data (K → D):

• Input: Patient's known medical history and other available test results.

• Output: Infer missing blood count data, such as “white blood cell count likely ranges between 8,000 and 10,000.”

• Implementation: Utilize the knowledge graph to map historical symptoms to missing data points.

• Action: Use the medical knowledge base to fill in missing data.

• Example:

2. Semantic Contextual Completion (I → K → D):

• Input: Real-time test results indicating elevated inflammation markers.

• Inference: Predict missing CRP levels are above normal using semantic modeling.

• Action: Extract context-related information from the semantic space to infer missing data.

• Example:

3. Intent-Driven Prioritization (W → P):

• Intent: Prioritize life-threatening indicators.

• Implementation: Skip minor tests and infer only critical CRP levels for severe infection risks.

• Action: Focus on completing critical data based on intent.

• Example:

2.2 Inconsistent Problem

Problem Description:

• Conflicting data from imaging tests showing normal lungs versus lab tests indicating elevated CRP levels, suggesting infection.

Solution Pathways:

1. Conflict Resolution via Wisdom (W → K):

• Input: Imaging report reliability 70%, lab test reliability 80%.

• Output: Estimate infection likelihood based on higher-weighted lab tests.

• Action: Use wisdom layer weighting mechanisms to resolve conflicts.

• Example:

2. Intent-Driven Priority Selection (P → I):

• Intent: Quickly identify high-risk infections.

• Implementation: Favor lab test results and recommend imaging re-evaluation.

• Action: Adjust data prioritization based on the goal.

• Example:

3. Knowledge Logic Reconstruction (K → K):

• Add logic: “When lab and imaging results conflict, prioritize lab data but recommend further confirmation.”

• Action: Update the knowledge graph to handle conflicts dynamically.

• Example:

2.3 Imprecise Problem

Problem Description:

• Vague symptom descriptions such as "likely a mild infection," lacking clear severity or location details.

Solution Pathways:

1. Semantic Fuzzy Reasoning (I → K → W):

• Input: Vague symptom description of “mild infection.”

• Reasoning: Combine historical data to determine a more specific judgment: “Possibly a respiratory tract infection.”

• Action: Allow fuzzy reasoning in the semantic space to generate actionable insights.

• Example:

2. Intent-Driven Clarification (P → W → I):

• Intent: Initiate treatment promptly.

• Output: Recommend starting broad-spectrum antibiotics for respiratory infections.

• Action: Translate imprecise information into actionable suggestions guided by intent.

• Example:

3. Data Feedback Optimization (W → D):

• Notify staff to collect additional samples (e.g., throat swabs) for confirmation.

• Action: Use wisdom to optimize data collection, reducing vagueness.

• Example:

3. Mathematical Interpretation: Intent-Driven Dynamic Optimization3.1 Optimization Objective Function

T=fP(D,I,K,W)T = f_P(D, I, K, W)T=fP​(D,I,K,W)

• T: The final diagnostic recommendation.

• f_P: Dynamic optimization function driven by intent, balancing interactions between layers.

3.2 Dynamic Weight Adjustment

W(eij)=g(P,Rij)W(e_{ij}) = g(P, R_{ij})W(eij​)=g(P,Rij​)

• W(e_{ij}): Weight of the path from layer $i$ to $j$.

• P: Intent priority.

• R_{ij}: Contextual relevance of transformation rules.

3.3 Compensation and Validation Paths

• Compensation Formula:Dcompensation=Khistory+IcontextD_{\text{compensation}} = K_{\text{history}} + I_{\text{context}}Dcompensation​=Khistory​+Icontext​

• Use historical knowledge and contextual information to complete missing data.

• Validation Formula:Ivalidation=Wweighted⋅IconflictI_{\text{validation}} = W_{\text{weighted}} \cdot I_{\text{conflict}}Ivalidation​=Wweighted​⋅Iconflict​

• Apply wisdom-driven weighted mechanisms to validate conflicting inputs.

4. Core Mechanisms of DIKWP: Mutual Compensation and Validation4.1 Mutual Compensation

• Knowledge Complements Data: Historical knowledge and semantic context fill gaps in incomplete data.

• Semantic Space Enhances Knowledge: Fuzzy or vague inputs transform into actionable knowledge.

4.2 Validation Mechanism

• Wisdom Validates Data: Weighted selection of the most reliable information source.

• Logic Validates Knowledge: Dynamic updates to knowledge rules ensure consistency.

5. Conclusion and Extensions

By leveraging DIKWP's dynamic transformation mechanism, the intelligent medical system efficiently addresses 3-No Problems in uncertain, incomplete, or conflicting scenarios. The model's strengths include:

1. Intent-Driven Focus: Prioritizes goals to guide dynamic decision-making.

2. Dynamic Compensation: Fills data gaps and resolves conflicts using semantic and knowledge-based transformations.

3. Efficient Validation: Uses wisdom-layer mechanisms for quick, accurate validation.

Extended Applications

• Disaster Response: Rapidly formulate rescue plans.

• Traffic Management: Optimize signal timings for smoother flow.

• Intelligent Recommendations: Enhance personalized user experiences.

The DIKWP model's universality and adaptability offer a revolutionary framework for solving complex problems under open-world assumptions.