The role of domain-specific knowledge in the planning as satisfiability framework
Henry Kautz, Bart Selman · 1998
SATPLAN is currently one of the fastest planning systems for 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 greatly enhances the power of SATPLAN: solution times for some problems are reduced from days to seconds. Introduction Planning is a notoriously hard combinatorial search problem. In Kautz and Selman (1996) we considered domain-independent, state-space planning as a computational challenge. This style of planning is a core problem in AI; similar computati...