Definition and Key Insights of the 3-No Problem
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：[email protected]）
1. Definition and Key Insights of the 3-No Problem(1) Essence of the Problem
The 3-No problem is a class of issues recognized under the Open World Assumption (OWA), which challenges traditional reasoning methods in cognitive systems. These problems are defined by incompleteness, inconsistency, and imprecision, and cannot be effectively solved using the conventional Closed World Assumption (CWA).
The key insight here is that solving these problems does not rely on the pursuit of precision, completeness, or consistency in individual components of the DIKWP framework (Data, Information, Knowledge, Wisdom, Purpose). Instead, it is driven by a Purpose-Driven approach that adjusts and generates solutions dynamically through the semantic space to meet the goals, regardless of exactness in the concept space.
(2) Core Insight from Professor Duan
Existence in the Semantic Space: The solution to the 3-No problem relies on semantic generation and manipulation within the semantic space, rather than requiring precision or completeness in the concept space.
Purpose-Driven: The approach is goal-centric, where the goal is mapped to the semantic space, and the solution is constructed through dynamic and fuzzy matching to achieve the desired outcome, rather than focusing on exact knowledge or data completeness.
2. Dynamic Nature of the DIKWP Network Model
(1) Definition of the Network Model
DIKWP is a dynamic network cognitive model consisting of five core elements:
D (Data): Basic entities representing “sameness.”
I (Information): Semantic relationships between data, representing “difference.”
K (Knowledge): Structured expression of information, representing “completeness.”
W (Wisdom): Dynamic decision-making ability based on knowledge.
P (Purpose): The goal and direction driving the DIKWP transformation process.
(2) Transformation Mechanism
The transformation within the network model is multi-dimensional and dynamic:
5×5 Transformation Matrix: Each element can serve as an input or output, resulting in 25 potential interaction paths.
Bidirectional Dynamic Feedback: Outputs can feedback into inputs, forming a closed-loop in cognition and reasoning.
3. Manifestations of the 3-No Problem in the DIKWP Network Model
The 3-No problem manifests differently across the various levels of DIKWP and their interaction paths. Here is an analysis of each problem type and its corresponding solution path within the DIKWP framework:
(1) Incompleteness (Incomplete)
Manifestation:
Missing data: Some data points are not captured.
Insufficient information: Lack of completeness in the relationships between data.
Knowledge gaps: Missing essential background knowledge.
Solution Strategy:
Transformation Path: W → P
Transformation Path: K → D
Example: Predicting future weather changes from a weather model.
Transformation Path: D → I → K
Example: Inferring global trends from local samples in a dataset.
Semantic Space Completion: Generate the missing part based on existing data through semantic association.
Knowledge-Driven Data: Inferring missing data from a knowledge base.
Wisdom-Based Decision Optimization: Directly generate decisions that satisfy the goal, without needing to fully complete the data.
(2) Inconsistency (Inconsistent)
Manifestation:
Data conflicts: Contradictory results from different data sources.
Information contradictions: Multiple inconsistent descriptions of the same event.
Knowledge logical conflicts: Contradictions between inference rules.
Solution Strategy:
Transformation Path: K → K
Transformation Path: P → I
Example: In disaster management, prioritize data from critical regions.
Transformation Path: W → K
Example: In case of conflicting sensor data, use wisdom-based mechanisms to select the more reliable data source.
Wisdom Layer Reconciliation: Resolve conflicts through weighting mechanisms or conflict resolution in the wisdom space.
Purpose-Driven Selection: Choose the most trustworthy information based on the goal.
Knowledge Logical Reconstruction: Adjust the logical relationships between knowledge and generate new inference paths.
(3) Imprecision (Imprecise)
Manifestation:
Fuzzy data: Input data contains errors or uncertainties.
Fuzzy information: Descriptions are difficult to interpret clearly.
Fuzzy decision-making: Inference results lack clarity.
Solution Strategy:
Transformation Path: W → D
Transformation Path: P → W → I
Example: Refining market strategy based on fuzzy consumer feedback.
Transformation Path: I → K → W
Example: In sentiment analysis, generating a general sentiment from fuzzy user input.
Semantic Fuzzy Processing: Allow for fuzzy reasoning and generate approximate solutions in the semantic space.
Goal-Oriented Precision: Refine fuzzy results based on goals, generating a precise solution.
Data Feedback Optimization: Optimize data collection and processing based on preliminary fuzzy results.
4. Mathematical Formalization and Transformation Model
(1) Purpose-Driven Transformation Function
Define the goal function:
T=fP(D,I,K,W,P)T = f_P(D, I, K, W, P)T=fP(D,I,K,W,P)
T: The semantic space solution generated based on the goal.
f_P: The purpose-driven transformation function that adjusts the interaction between the DIKWP elements.
(2) Path Weight Optimization
Define the optimization of path weights:
W(eij)=g(P,Rij)W(e_{ij}) = g(P, R_{ij})W(eij)=g(P,Rij)
W(e_{ij}): The weight of the transformation path from iii to jjj.
P: The priority of the goal in the purpose space.
R_{ij}: The contextual relevance of the current transformation rule.
5. Key Features of the 3-No Problem Solution
Dynamic Adaptability:
The transformation mechanism allows for solutions that meet the goal in the presence of incomplete, inconsistent, or imprecise input.
Goal-Centric:
Focuses on achieving the goal through the transformation process, adjusting interactions based on purpose.
Deviation from Formalization:
No longer bound by traditional precision, completeness, or consistency in the concept space, instead constructing “existence-based” solutions in the semantic space.
6. Application Scenarios
Medical Diagnosis: Handling incomplete patient data and generating dynamic diagnostic suggestions.
Disaster Management: Prioritizing key areas’ data in the presence of inconsistent sensor readings.
Market Analysis: Extracting actionable strategies from fuzzy consumer feedback.
Conclusion
Professor Yucong Duan’s 3-No problem addresses challenges that arise from the limitations of traditional cognitive frameworks based on the Closed World Assumption (CWA). By focusing on a Purpose-Driven approach and utilizing semantic space, the DIKWP Network Model provides a flexible and effective solution to these challenges. This theory not only breaks through the constraints of precise knowledge but also offers valuable insights for the future development of artificial intelligence and artificial consciousness.