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Encoding Domain and Control Knowledge for Propositional Planning

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Logic-Based Artificial Intelligence

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 597))

Abstract

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.

This is a revised and expanded version of a paper which appeared in the Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems (AIPS-98), Pittsburgh, PA, 1998.

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Author information

Authors and Affiliations

  1. University of Washington, Seattle, Washington, 98195, USA

    Henry Kautz

  2. Cornell University, Ithaca, NY, 14853, USA

    Bart Selman

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  1. Henry Kautz
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  2. Bart Selman
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Editors and Affiliations

  1. Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland, USA

    Jack Minker

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Kautz, H., Selman, B. (2000). Encoding Domain and Control Knowledge for Propositional Planning. In: Minker, J. (eds) Logic-Based Artificial Intelligence. The Springer International Series in Engineering and Computer Science, vol 597. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1567-8_8

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  • DOI: https://doi.org/10.1007/978-1-4615-1567-8_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5618-9

  • Online ISBN: 978-1-4615-1567-8

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