Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multi-agent oriented constraint satisfaction
Artificial Intelligence
Constraint Modeling in the Context of Academic Task Assignment
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Hi-index | 0.00 |
We propose an improved restart strategy for randomized backtrack search, and evaluate its performance by comparing to other heuristic and stochastic search techniques for solving random problems and a tight real-world resource allocation problem. The restart strategy proposed by Gomes et al. [1] requires the specification of a cutoff value determined from an overall profile of the cost of search for solving the problem. When no such profile is known, the cutoff value is chosen by trial-and-error. The Randomization and Geometric Restart (RGR) proposed by Walsh does not rely on a cost profile but determines the cutoff value as a function of a constant parameter and the number of variables in the problem [2]. Unlike these strategies, which have fixed restart schedules, our technique (RDGR) dynamically adapts the value of the cutoff parameter to the results of the search process. Our experiments investigate the behavior of these techniques using the cumulative distribution of the solutions, over different run-time durations, values of the cutoff, and problem types. We show that distinguishing between solvable and over-constrained problem instances yields new insights on the relative performance of the search techniques tested. We propose to use this characterization as a basis for building new strategies of cooperative, hybrid search.