Event Selection Rules to Compute Explanations
This work addresses an incremental improvement in constraint satisfaction problem solving by enhancing explanation computation for specific backtracking algorithms.
The paper tackles the challenge of efficiently implementing explanations in CSP solvers by introducing ESeR, an Event Selection Rules algorithm that dynamically filters propagation events, resulting in effective computation of explanations for intelligent backtracking algorithms as demonstrated on MiniZinc challenge instances.
Explanations have been introduced in the previous century. Their interest in reducing the search space is no longer questioned. Yet, their efficient implementation into CSP solver is still a challenge. In this paper, we introduce ESeR, an Event Selection Rules algorithm that filters events generated during propagation. This dynamic selection enables an efficient computation of explanations for intelligent backtracking al- gorithms. We show the effectiveness of our approach on the instances of the last three MiniZinc challenges