Abductive Reasoning Apparatus Constraint Segmentation
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Solution Overview
Problem
Existing abductive reasoning techniques face inefficiencies due to the lengthy computation time required to list and process a large number of logical constraints, which acts as a bottleneck and increases reasoning time.
Innovation Solution
An abductive reasoning apparatus that acquires background knowledge and query information, generates hypotheses, constructs a constraint set by listing some logical constraints, retrieves candidate solution hypotheses, determines satisfaction of additional constraints, and outputs solutions, thereby reducing the reliance on processing all constraints simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all logical constraints are listed and processed simultaneously, then the completeness of constraint satisfaction is improved, but the computation time and processing efficiency deteriorate
Solution Approach 1:
The patent segments the constraint satisfaction process into two distinct phases: (1) generating candidate hypotheses using a subset of constraints, and (2) verifying these candidates against the remaining constraints. This segmentation allows the system to avoid processing all constraints simultaneously, thereby reducing computation time while maintaining complete constraint satisfaction through the verification phase.
Solution Approach 2:
The patent performs preliminary filtering by generating candidate hypotheses using a selected subset of constraints before final verification. This preliminary action reduces the search space for the optimization solver, allowing it to work with fewer constraints initially, thus improving efficiency while the subsequent verification ensures completeness.
2Productivity
If a subset of logical constraints is used for generating candidate hypotheses, then the computation efficiency is improved, but the risk of missing valid constraints increases
Solution Approach 1:
The patent implements a feedback mechanism where candidate hypotheses generated from a subset of constraints are verified against the remaining constraints. If a candidate fails to satisfy any constraint in the second set, it is rejected. This feedback loop ensures that using a subset of constraints for generation does not compromise the completeness of constraint satisfaction, as the verification phase catches any violations.
3Measurement precision
If more logical constraints are included in the constraint set, then the accuracy of solution hypothesis is improved, but the complexity of constraint processing increases
Solution Approach 1:
The patent divides the constraint set into two segments: constraints used for generating candidate hypotheses and constraints used for verifying them. This segmentation reduces the complexity of the constraint processing at any given time, as the optimization solver only needs to handle a subset of constraints during generation, while the verification phase ensures accuracy by checking against all constraints.
Data Source
AI summary
An abductive reasoning apparatus (10) includes: a generation section (12) that generates a plurality of hypotheses with reference to background knowledge information and query information; a construction section (13) that constructs a constraint set by listing some of a plurality of logical constraints to be satisfied between a plurality of elements constituting each of the plurality of hypotheses; a retrieval section (14) that retrieves any one of the plurality of hypotheses as a candidate solution hypothesis with reference to the constraint set; a determination section (15) that determines whether the candidate solution hypothesis satisfies a logical constraint which is among logical constraints to be satisfied between elements constituting the candidate solution hypothesis and which is not included in the constraint set; and an output section (16) that outputs the candidate solution hypothesis as a solution hypothesis in a case where the candidate solution hypothesis satisfies the logical constraint.