Many research studies have shown that the choice of a relevant order of variables and/or values can significantly improve the efficiency of search algorithms. However, in Constraint Satisfaction Problems CSPs, constraint ordering heuristics have received less attention than variables and values ordering heuristics. In the literature, only some few contributions have been done on this area, and mainly as a preprocessing phase before starting the search process. In this paper, we show that constraint ordering heuristics are promising and able to make relevant choices during the search process within a Dynamic CSP. Our proposed approach, called Dynamic Constraint Ordering heuristic DCO, selects dynamically constraints which are likely to lead to conflicts. So, we apply a "fail first" principle, in a manner that a dynamic intelligent backtracking event can be performed earlier. This method is able to guide a dynamic repair algorithm towards the hardest constraint subproblems and to tackle inconsistencies in order to correct solution assignment. The conducted experiments have confirmed that the proposed approach is interesting. Our results show clearly the efficiency of the heuristics combined with Partial-Order Dynamic Backtracking
Part 4: Hybrid - Changing EnvironmentsInternational audienceFor a better treatment of Dynamic Constraint Satisfaction Problems (DCSPs), several techniques have been developed to be used in repair algorithms. We cite, for example, the variables/values ordering heuristics and local search techniques.We distinguish between static heuristics, which calculate their values once at the beginning of the search, and dynamic heuristics that use an expensive intelligence in terms of solving time.In this paper, we propose a new static variable ordering heuristic, Profound Degree (pdeg), based on deg heuristic, which calculates the degree of influence of a given variable, on the whole constraints network, relatively to its position in the network.We evaluate this heuristic on the Extended Partial-order Dynamic Backtracking (EPBD) approach, which is an approach to repair DCSPs solutions, and we compare it to the best-known variables ordering heuristics (VOHs) for repairing. The evaluation of performance is on random binary problems and meeting scheduling problems, with the criteria of computation time, number of constraints checks and Hamming distance between the former and the current solution
Extended Partial Dynamic Backtracking (EPDB) is a repair algorithm based on PDB. It deals with Dynamic CSPs based on ordering heuristics and retroactive data structures, safety conditions, and nogoods which are saved during the search process. In this paper, we show that the drawback of both EPDB and PDB is the exhaustive verification of orders, saved in safety conditions and nogoods, between variables. This verification affects remarkably search time, especially since orders are often indirectly deduced. Therefore, we propose a new approach for dynamically changing environments, the Lazy Repairing Backtracking (LRB), which is a fast version of EPDB insofar as it deduces orders directly through the used ordering heuristic. We evaluate LRB on various kinds of problems, and compare it, on the one hand, with EPDB to show its effectiveness compared to this approach, and, on the other hand, with MAC-2001 in order to conclude, from what perturbation rate resolving a DCSP with an efficient approach can be more advantageous than repair.
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