Reformulating Constraint Models for Classical Planning

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Constraint satisfaction techniques are commonly used for solving scheduling problems, still they are rare in AI planning. Although there are several attempts to apply constraint satisfaction for solving AI planning problems, these techniques never became predominant in planning; and they never reached the success of, for example, SAT-based planners. In this paper we argue that existing constraint models for classical AI planning are not fully using the power of constraint satisfaction; thus we propose a reformulation, which significantly improves their efficiency.