SAT Solver Clause Database Reuse for Dynamic Planning
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Solution Overview
Problem
Existing SAT solvers face inefficiencies in finding optimal solutions for planning problems due to the need to rediscover and relearn clauses at each fitness level, leading to significant time consumption in complex resource assignment and employment scenarios.
Innovation Solution
The method involves reusing elements of previously obtained partial solutions by analyzing modifications, identifying unaffected learned clauses, and retaining information based on unmodified original clauses to adapt efficiently to changed fitness thresholds, allowing for dynamic addition and removal of variables and clauses during the solving process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SAT solvers are repeatedly executed with increasing fitness thresholds to find optimal solutions, then solution optimality is achieved, but time consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-learning clauses and storing them in a clause database during initial SAT solver executions. When the solver is re-executed with higher fitness thresholds, these pre-learned clauses are reused, eliminating the need to relearn them from scratch. This preliminary preparation significantly reduces time consumption in subsequent optimization iterations while maintaining solution optimality.
Solution Approach 2:
The patent implements discarding and recovering by selectively retaining useful learned clauses in a database across multiple SAT solver executions. Instead of discarding all learned information after each execution, the system recovers and reuses valid clauses that remain applicable at higher fitness levels, thereby reducing redundant computation and time consumption in finding optimal solutions.
2Measurement precision
If SAT solvers learn and store clauses for each fitness level, then solution quality improves, but memory usage and processing overhead increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting which clauses are retained and reused based on the current fitness threshold. The system modifies the clause database contents adaptively, keeping only those clauses that remain valid and useful at the current optimization level. This selective parameter adjustment improves solution quality while minimizing unnecessary processing overhead and memory usage.
3Adaptability or versatility
If the constraint system is modified to reflect environmental changes, then adaptability to new conditions is achieved, but the time to resolve the modified problem increases
Solution Approach 1:
When the constraint system is modified to reflect environmental changes, the patent applies discarding and recovering by identifying which learned clauses remain valid and which become obsolete. The system recovers and retains only the clauses that are still applicable to the modified problem, discarding those that are invalidated by the changes. This selective recovery significantly reduces the time required to resolve the modified problem while maintaining full adaptability to new conditions.
Data Source
AI summary
A plan representing a final solution to a problem is obtained efficiently, if the problem changes while being solved, by identifying the solution elements not affected by the change, and by reusing those solution elements.