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A token-flow paradigm for verification of rule-based expert systems
Summary
This study introduces a novel graph-based method for verifying rule-based systems (RBSs). It efficiently detects anomalies in propositional and first-order logic systems using a token-flow paradigm on rule-dependency graphs.
Area of Science:
- Artificial Intelligence
- Computer Science
- Software Engineering
Background:
- Rule-based systems (RBSs) are widely used in expert systems and AI.
- Verifying the correctness and identifying anomalies in RBSs is crucial for reliable performance.
- Existing graph-based verification methods often have limitations regarding the types of rules they can handle.
Purpose of the Study:
- To present a novel graph-based approach for the verification of rule-based systems.
- To introduce a method for detecting explicit and potential anomalies within RBSs.
- To enable efficient run-time validation of rule-based systems.
Main Methods:
- Introduction of the rule-dependency graph (RDG) to model dependencies between rules in an RBS.
- Utilizing a token-flow paradigm to search for specific topological structures representing improper knowledge within the RDG.
- Development of an algorithm for automatic generation of minimally sufficient test tokens.
Main Results:
- The proposed method successfully identifies specific topological structures indicative of improper knowledge.
- The approach is applicable to non-Horn clause forms in both propositional and first-order logic.
- Overcomes limitations of other graph-based verification techniques.
Conclusions:
- The novel RDG and token-flow approach provide an effective method for RBS verification.
- This technique allows for the detection of various anomalies and facilitates efficient run-time validation.
- The method enhances the reliability and correctness of rule-based systems.
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