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  • Cost-Optimal Planning using Weighted MaxSAT

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    Author(s)
    Robinson, N
    Gretton, C
    Pham, DN
    Sattar, A
    Griffith University Author(s)
    Sattar, Abdul
    Pham, Nghia N.
    Robinson, Nathan M.
    Year published
    2010
    Metadata
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    Abstract
    We consider the problem of computing optimal plans for propositional planning problems with action costs. In the spirit of leveraging advances in general-purpose automated reasoning for that setting, we develop an approach that operates by solving a sequence of partial weighted MaxSAT problems, each of which corresponds to a step-bounded variant of the problem at hand. Our approach is the first SAT-based system in which a proof of cost-optimality is obtained using a MaxSAT procedure. It is also the first system of this kind to incorporate an admissible planning heuristic. We perform a detailed empirical evaluation of our ...
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    We consider the problem of computing optimal plans for propositional planning problems with action costs. In the spirit of leveraging advances in general-purpose automated reasoning for that setting, we develop an approach that operates by solving a sequence of partial weighted MaxSAT problems, each of which corresponds to a step-bounded variant of the problem at hand. Our approach is the first SAT-based system in which a proof of cost-optimality is obtained using a MaxSAT procedure. It is also the first system of this kind to incorporate an admissible planning heuristic. We perform a detailed empirical evaluation of our work using benchmarks from a number of International Planning Competitions.
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    Conference Title
    COPLAS 2010 - Proceedings of the Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems
    Publisher URI
    http://www.aaai.org/Press/Proceedings/icaps10.php
    Copyright Statement
    © 2010 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Use hypertext link for access to conference website.
    Subject
    Knowledge Representation and Machine Learning
    Publication URI
    http://hdl.handle.net/10072/34882
    Collection
    • Conference outputs

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