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Automated Creation of Pattern Database Search Heuristics

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Model Checking and Artificial Intelligence (MoChArt 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4428))

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Abstract

Pattern databases are dictionaries for heuristic estimates storing state-to-goal distances in state space abstractions. Their effectiveness is sensitive to the selection of the underlying patterns. Especially for multiple and additive pattern databases, the manual selection of patterns that leads to good exploration results is involved.

For automating the selection process, greedy bin-packing has been suggested. This paper proposes genetic algorithms to optimize its output. Patterns are encoded as binary strings and optimized using an objective function that predicts the heuristic search tree size based on the distribution of heuristic values in abstract space.

To reduce the memory requirements we construct the pattern databases symbolically. Experiments in heuristic search planning indicate that the total search efforts can be reduced significantly.

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Authors and Affiliations

  1. Computer Science Department, University of Dortmund,  

    Stefan Edelkamp

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  1. Stefan Edelkamp
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Stefan Edelkamp Alessio Lomuscio

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Edelkamp, S. (2007). Automated Creation of Pattern Database Search Heuristics. In: Edelkamp, S., Lomuscio, A. (eds) Model Checking and Artificial Intelligence. MoChArt 2006. Lecture Notes in Computer Science(), vol 4428. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74128-2_3

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  • DOI: https://doi.org/10.1007/978-3-540-74128-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74127-5

  • Online ISBN: 978-3-540-74128-2

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