Inconsistency Detection in Decision Graphs for Hypothetical Reasoning
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Solution Overview
Problem
Existing hypothetical reasoning systems do not detect inconsistencies between observed events and knowledge, leading to potential waste and incorrect results when deriving hypotheses.
Innovation Solution
An inconsistency detection device and method that constructs a decision graph from a knowledge base, accepts observed events, deletes edges indicating transitions to negation sides, and determines if a path to a true-valued leaf node exists, deciding inconsistency based on the absence of such a path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hypothetical reasoning is performed without inconsistency detection, then reasoning speed is maintained, but incorrect hypotheses may be derived and time is wasted on useless reasoning
Solution Approach 1:
The inconsistency detection device performs preliminary checks on observed events before they are fully processed in the hypothetical reasoning system. By detecting inconsistencies between observed events and the knowledge base in advance, the system prevents wasted computation time on obviously invalid reasoning paths, thereby improving overall efficiency while maintaining reliability
2Reliability
If inconsistency detection is implemented, then reliability of hypothesis derivation is improved, but device complexity increases
Solution Approach 1:
The inconsistency detection device acts as an intermediary component between the knowledge base and the hypothetical reasoning engine. It provides a dedicated inconsistency detection function that filters out contradictory observed events before they enter the main reasoning process, thereby improving reliability without significantly complicating the core reasoning system
Solution Approach 2:
The reasoning system is segmented into distinct functional modules: the knowledge base, the inconsistency detection device, and the hypothetical reasoning engine. This segmentation allows the inconsistency detection function to be developed and optimized independently, improving reliability while managing system complexity through modular architecture
Data Source
AI summary
The acceptance means 81 accepts input of a series of observed events including one or more observed events. The deletion means 82 deletes, in a decision graph for determining a boolean value of the observed event based on a knowledge base in which the observed event is a node and transition relationship of each of the observed events is an edge, the edge indicating a transition to a negation side of the observed event from among the edges connected to the node corresponding to the observed event accepted by the acceptance means 81. The determination means 83 determines whether or not a path from a root node to a true-valued leaf node exists in the decision graph after the deletion of the edge by the deletion means 82. The decision means 84 decides that the series of observed events is inconsistent with respect to the knowledge base when the determining means 83 determines that there is no path to the leaf node indicating the true value.