Encoding domain knowledge for propositional planning

Propositional satisfiability checking is a powerful approach to domain-independent planning. In nearly all practical applications, however, there exists an abundance of domain-specific knowledge that can be used to improve the performance of a planning system. This knowledge is traditionally encoded as procedures or rules that are tied to the details of the planning engine. We present a way to encode domain knowledge in a purely declarative, algorithm independent manner. We demonstrate that the same heuristic knowledge can be used by completely different search engines, one systematic, the other using greedy local search. This approach enhances the power of planning as satisfiability : solution times for some problems are reduced from days to seconds.

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