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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
Generic SAT solvers have been very successful in solving hard combinatorial problems in various application areas, including AI planning. There is potential for improved performance by making the SAT solving process more application-specific. In this paper we propose a variable selection strategy for AI planning. The strategy is based on generic principles about properties of plans, and its performance with standard planning benchmarks often substantially improves on generic variable selection<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-15396-9_34">doi:10.1007/978-3-642-15396-9_34</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zovmyaqwsva6joxozsy3g4orke">fatcat:zovmyaqwsva6joxozsy3g4orke</a> </span>
more »... euristics used in SAT solving, such as the VSIDS strategy. These improvements lift the efficiency of SAT based planning to the same level as best planners that use other search methods. Planning as Satisfiability The classical planning problem involves finding a sequence of actions from a given initial state to a goal state. The actions are deterministic, which means that an action and the current state determine the successor state uniquely. In the simplest formalization of planning actions are pairs (p, e) where p and e are consistent
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