Abstract
Macro-operators are sequences of actions that can guide a planner to achieve its goals faster by avoiding search for those sequences. However, using macro-operators will also increase the branching factor of choosing operators, and as a result making planning more complex and less efficient. On the other hand, the detection and exploitation of symmetric structures in planning problems can reduce the search space by directing the search process. In this paper, we present a new method for detecting symmetric objects through subgraph-isomorphism, and exploiting the extracted information in macro-operator selection. The method has been incorporated into HSP2, and tested on a collection of different planning domains.
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Houshmandan, A., Ghassem-Sani, G., Nakhost, H. (2007). Integration of Symmetry and Macro-operators in Planning. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_101
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DOI: https://doi.org/10.1007/978-3-540-76631-5_101
Publisher Name: Springer, Berlin, Heidelberg
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