Fast planning through planning graph analysis
Artificial Intelligence
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Reduction operations in fuzzy or valued constraint satisfaction
Fuzzy Sets and Systems - Optimisation and decision
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Existential arc consistency: getting closer to full arc consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
MINIMAXSAT: an efficient weighted max-SAT solver
Journal of Artificial Intelligence Research
MiniMaxSAT: a new weighted Max-SAT solver
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
A weighted CSP approach to cost-optimal planning
AI Communications
Boolean lexicographic optimization: algorithms & applications
Annals of Mathematics and Artificial Intelligence
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We show in this article how the Weighted CSP framework can be used to solve an optimisation version of numerical planning. The WCSP finds an optimal plan in the planning graph containing all solution plans of minimum length. Experimental trials were performed to study the impact of soft arc consistency techniques (FDAC and EDAC) on the efficiency of the search for an optimal plan in this graph. We conclude by giving a possible theoretical explanation for the fact that we were able to solve optimisation problems involving several hundred variables.