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  • Engineering Efficient Planners with SAT

    Author(s)
    Rintanen, Jussi
    Griffith University Author(s)
    Rintanen, Jussi
    Year published
    2012
    Metadata
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    Abstract
    Planning with SAT has long been viewed as a main approach to AI planning. In comparison to other approaches, its high memory requirements have been considered to be a main obstacle to its scalability to large planning problems. Better implementation technology, especially addressing the memory use, together with a shift of understanding about SAT-based planning during the past ten years, enables planners that radically differ from those from the late 1990s. We discuss a SAT-based planning system that implements modern versions of virtually all components of first planners that used SAT, focusing on the new implementation ...
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    Planning with SAT has long been viewed as a main approach to AI planning. In comparison to other approaches, its high memory requirements have been considered to be a main obstacle to its scalability to large planning problems. Better implementation technology, especially addressing the memory use, together with a shift of understanding about SAT-based planning during the past ten years, enables planners that radically differ from those from the late 1990s. We discuss a SAT-based planning system that implements modern versions of virtually all components of first planners that used SAT, focusing on the new implementation technology for a compact clause representation that is both simpler and more effective than ones proposed earlier. Specifically, the decreased memory requirements enable the use of top-level solution strategies that lift the performance of SAT-based planning to the same level with other search methods.
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    Conference Title
    Frontiers in Artificial Intelligence and Applications: Proceedings of the 20th European Conference on Artificial Intelligence ECAI 2012
    Publisher URI
    http://www2.lirmm.fr/ecai2012/
    DOI
    https://doi.org/10.3233/978-1-61499-098-7-684
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/47126
    Collection
    • Conference outputs

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