A Variable Depth Search Algorithm for Binary Constraint Satisfaction Problems

Abstract

The constraint satisfaction problem (CSP) is a popular used paradigm to model a wide spectrum of optimization problems in artificial intelligence. This paper presents a fast metaheuristic for solving binary constraint satisfaction problems. The method can be classified as a variable depth search metaheuristic combining a greedy local search using a self-adaptive weighting strategy on the constraint weights. Several metaheuristics have been developed in the past using various penalty weight mechanisms on the constraints. What distinguishes the proposed metaheuristic from those developed in the past is the update ofkvariables during each iteration when moving from one assignment of values to another. The benchmark is based on hard random constraint satisfaction problems enjoying several features that make them of a great theoretical and practical interest. The results show that the proposed metaheuristic is capable of solving hard unsolved problems that still remain a challenge for both complete and incomplete methods. In addition, the proposed metaheuristic is remarkably faster than all existing solvers when tested on previously solved instances. Finally, its distinctive feature contrary to other metaheuristics is the absence of parameter tuning making it highly suitable in practical scenarios.

Fast constraint satisfaction problem and learning-based algorithm for solving Minesweeper

“…We also showed that deep Q-learning is marginally better than machine learning (classification) methods in general, but not always as can be seen in the Table (4). Overall, we claim that our method of deep Q-learning (Version 6.5) can play Minesweeper to a high degree of accuracy while still being fast enough to play boards as large as 200 total blocks.…”

“…CSP is another prominent way of formulating the Minesweeper game in a mathematical form and several algorithms [22] exist to solve these CSPs. CSPs are the subject of intense research in both AI and operations research since not only it overcomes the problems in single-point strategy, but the regularity in their formulation provides a common basis to analyze and solve problems of many unrelated families [11,4]. MDP is another way of formulating games into state-action-reward form.…”

Dynamic Initial Weight Assignment for MaxSAT

“…Ref. [35], BalancedZ [36], Score 2 SAT [37], CCAnrSim [38], CryptoMiniSAT [39], Sparrow [27], MapleCOMSPS_LRB_VISDIS [39], and previously DLM [13], SAPS [14], PAWS [15], and DDFW [28,40]).…”

A Multilevel Genetic Algorithm for the Maximum Satisfaction Problem

“…The central idea is to reduce the size of local search space relying on a continual relaxation (removing elements from the solution) and re-optimization (re-inserting the removed elements). Finally, the work introduced in [16] introduces a variable depth metaheuristic combing a greedy local search with a self-adaptive weighting strategy on the constraints weights.…”