Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem
Journal of Automated Reasoning
Adaptive tree search
Approximate counting by sampling the backtrack-free search space
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Using expectation maximization to find likely assignments for solving CSP's
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
Deriving Information from Sampling and Diving
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Efficient generic search heuristics within the EMBP framework
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
A new algorithm for sampling CSP solutions uniformly at random
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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We investigate the impact of information extracted from sampling and diving on the solution of Constraint Satisfaction Problems (CSP). A sample is a complete assignment of variables to values taken from their domain according to a given distribution. Diving consists in repeatedly performing depth first search attempts with random variable and value selection, constraint propagation enabled and backtracking disabled; each attempt is called a dive and, unless a feasible solution is found, it is a partial assignment of variables (whereas a sample is a -possibly infeasible- complete assignment). While the probability of finding a feasible solution via sampling or diving is negligible if the problem is difficult enough, samples and dives are very fast to generate and, intuitively, even when they are infeasible, they can provide some statistical information on search space structure. The aim of this paper is to understand to what extent it is possible to support the CSP solving process with information derived from sampling and diving. In particular, we are interested in extracting from samples and dives precise indications on the quality of individual variable-value assignments with respect to feasibility. We formally prove that even uniform sampling could provide precise evaluation of the quality of single variable-value assignments; as expected, this requires huge sample sizes and is therefore not useful in practice. On the contrary, diving is much better suited for assignment evaluation purposes. We undertake a thorough experimental analysis on a collection of Partial Latin Square and Car Sequencing instances to assess the quality of information provided by dives. Dive features are identified and their impact on search is evaluated. Results show that diving provides information that can be fruitfully exploited.