Inference Device Parallel ILP SAT Solving Variable Order
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The time required to obtain an optimal solution for ILP or SAT problems in abductive reasoning varies significantly, even for problems of similar sizes, and it is unpredictable, leading to inefficient solving processes.
Innovation Solution
An inference device and method that generate equivalent ILP or SAT problems by rearranging the order of variables and solve them in parallel using multiple identical solvers, outputting the result from the solver that provides a solution first as the optimal solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hypothesis candidates are converted to an ILP problem or a SAT problem and solved using a solver, then an optimal solution can be obtained, but the time required to obtain the solution varies greatly and is unpredictable
Solution Approach 1:
The patent divides the single ILP/SAT problem into multiple equivalent problems by changing variable orders. Each equivalent problem is then solved by a separate solver instance in parallel, transforming one sequential solving process into multiple concurrent processes that collectively reduce total solving time while maintaining solution optimality.
Solution Approach 2:
The patent transitions from a single-dimension sequential solving approach to a multi-dimensional parallel processing approach. By generating equivalent problems with different variable orderings and solving them simultaneously across multiple solver instances, the system adds a temporal parallelism dimension that reduces overall solving time without sacrificing solution quality.
2Ease of operation
If the same ILP solver or SAT solver is used to solve different problems of similar sizes, then the solving process is consistent, but the time required to obtain solutions varies significantly
Solution Approach 1:
The patent changes the parameter of variable ordering in the ILP/SAT problems to generate equivalent problems. By systematically varying this parameter across multiple equivalent problem instances, the system explores different solving paths that may be more efficient for the solver, thereby improving solving speed while maintaining process consistency through the use of identical solver configurations.
Data Source
AI summary
In an inference device, an observation input means receives an observation as an input. A hypothesis candidate generation means generates hypothesis candidates by applying inference knowledge to the observation in the backward direction. The problem conversion means converts the hypothesis candidate into an ILP problem or a SAT problem. An equivalent problem generation means generates a specified number of equivalent ILP problems or equivalent SAT problems in which an order of the variables included in the converted ILP problem or SAT problem is changed. The solver parallelization means solves the generated equivalent ILP problems or equivalent SAT problems by executing a specified number of identical ILP solvers or SAT solvers in parallel. The optimal solution output means outputs a result of the ILP solver or the SAT solver that output the result first, among the specified number of ILP solvers or SAT solvers, as the optimal solution.