Constraint Enumeration and Redundant Constraint Deletion for Abduction Inference
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Solution Overview
Problem
The existing abduction inference method experiences a significant decrease in computation speed as the number of logic constraints increases, due to the representation of constraints as a constrained combinatorial optimization problem, leading to redundant constraints and extended inference computation time.
Innovation Solution
An information processing apparatus and method that includes a constraint enumeration unit, a redundant constraint deletion unit, and a candidate hypothesis conversion unit to identify and exclude redundant constraints, thereby generating a reduced combinatorial optimization problem that does not affect the inference result, improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all logic constraints are included in the constrained combinatorial optimization problem, then the completeness of hypothesis validation is improved, but the computation time increases markedly
Solution Approach 1:
The patent extracts and removes redundant constraints from the set of logic constraints before formulating the constrained combinatorial optimization problem. The constraint deletion unit identifies and eliminates constraints that do not affect the inference result, keeping only the essential constraints needed for valid hypothesis validation. This reduces the problem size and computation time while maintaining validation completeness.
Solution Approach 2:
The patent performs preliminary analysis of constraints before the optimization process. The constraint deletion unit pre-processes the logic constraints to identify redundant ones in advance, using graph analysis and logical deduction to determine which constraints can be safely removed. This preliminary filtering prevents unnecessary computation during the inference process.
2Measurement precision
If the number of logic constraints increases, then the accuracy of hypothesis filtering is improved, but the scale of the optimization problem increases leading to reduced productivity
Solution Approach 1:
The patent extracts only the necessary constraints from the full set of logic constraints. By analyzing the constraint graph and identifying redundant constraints, the system removes unnecessary constraints while preserving those essential for accurate hypothesis filtering. This maintains filtering accuracy while reducing the optimization problem scale.
Solution Approach 2:
The patent changes the parameter of constraint quantity by dynamically determining the optimal set of constraints to use. Instead of using all logic constraints, the system transforms the constraint set by removing redundancies, thereby changing the effective number of constraints from N to M where M < N, improving computational efficiency while maintaining filtering accuracy.
3Reliability
If redundant constraints are included in the optimization problem, then the completeness of constraint coverage is improved, but the device complexity and computation overhead increase
Solution Approach 1:
The patent extracts and removes redundant constraints from the optimization problem formulation. The constraint deletion unit analyzes the constraint graph to identify constraints that provide no additional filtering value and removes them, reducing problem complexity while maintaining complete coverage of necessary constraints for valid hypothesis inference.
Data Source
AI summary
An information processing apparatus comprises the constraint enumeration unit that enumerates, for a plurality of candidate hypotheses generated from a query formula and background knowledge, constraints to be satisfied by the candidate hypotheses, the redundant constraint deletion unit that searches for and deletes redundant constraints not to affect an inference result from the constraints enumerated by the constraint enumeration means, and the candidate hypothesis conversion unit generates a combinatorial optimization problem from the plurality of candidate hypotheses and a set of constraints enumerated by the constraint enumeration unit that remain after the deletion of redundant constraints by the redundant constraint deletion unit.