New heuristic order based on tree-decomposition for solving CSPs

The Constraint Satisfaction Problem(CSP)represents a pivotal area of study within artificial intelligence, offering a broad spectrum of applications from scheduling to resource allocation. Despite significant advancements in the development of algorithms and heuristics, the efficient resolution of large and intricate CSP instances continues to pose a considerable challenge to the research community. Traditional heuristics often fail to efficiently navigate the solution space, struggling to converge on optimal solutions within a reasonable timeframe. This paper introduces a novel heuristic, rooted in tree-decomposition techniques, specifically designed to enhance the efficiency of CSP solvers. Our approach leverages an innovative variable and value ordering strategy, which systematically reduces the search space and the computational demands. We conducted extensive experiments using a set of benchmark CSP instances to validate the efficacy of the proposed heuristic. The results demonstrate a marked improvement in solving efficiency, particularly evidenced in challenging instances from the modified-renault benchmark, such as renault-mod-4_ext and renault-mod-32_ext. These findings underscore the potential of our heuristic to significantly advance the state-of-the-art in CSP solving methodologies.