Generating Macro-operators by Exploiting Inner Entanglements

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Chrpa, Lukas ; Vallati, Mauro ; Kitchin, Diane E. ; McCluskey, Thomas Leo (2013)
  • Publisher: AAAI Press
  • Subject: QA75

In Automated Planning, learning and exploiting additional\ud knowledge within a domain model, in order\ud to improve plan generation speed-up and increase\ud the scope of problems solved, has attracted much research.\ud Reformulation techniques such as those based\ud on macro-operators or entanglements are very promising\ud because they are to some extent domain model and\ud planning engine independent. This paper aims to exploit\ud recent work on inner entanglements, relations between\ud pairs of planning operators and predicates encapsulating\ud exclusivity of predicate ‘achievements‘ or ‘requirements’,\ud for generating macro-operators. We provide\ud a theoretical study resulting in a set of conditions\ud when planning operators in an inner entanglement relation\ud can be removed from a domain model and replaced\ud by a macro-operator without compromising solvability\ud of a given (class of) problem(s). The effectiveness of\ud our approach will be experimentally shown on a set\ud of well-known benchmark domains using several highperforming\ud planning engines.
  • References (27)
    27 references, page 1 of 3

    Alhossaini, M., and Beck, J. C. 2009. Learning instancespecific macros. In ICAPS Workshop on Planning and Learning.

    Botea, A.; Enzenberger, M.; Mu¨ller, M.; and Schaeffer, J. 2005. Macro-FF: improving AI planning with automatically learned macro-operators. Journal of Artificial Intelligence Research (JAIR) 24:581-621.

    Bylander, T. 1994. The computational complexity of propositional STRIPS planning. Artificial Intelligence 69:165- 204.

    Chapman, D. 1987. Planning for conjunctive goals. Artificial Intelligence 32(3):333-377.

    Chen, Y., and Yao, G. 2009. Completeness and optimality preserving reduction for planning. In Proceedings of IJCAI, 1659-1664.

    Chrpa, L., and Barta´k, R. 2009. Reformulating planning problems by eliminating unpromising actions. In Proceedings of SARA, 50-57.

    Chrpa, L., and McCluskey, T. L. 2012. On exploiting structures of classical planning problems: Generalizing entanglements. In Proceedings of ECAI, 240-245.

    Chrpa, L. 2010a. Combining learning techniques for classical planning: Macro-operators and entanglements. In Proceedings of ICTAI, volume 2, 79-86.

    Chrpa, L. 2010b. Generation of macro-operators via investigation of action dependencies in plans. Knowledge Engineering Review 25(3):281-297.

    Coles, A. J., and Coles, A. I. 2010. Completeness-preserving pruning for optimal planning. In Proceedings of ECAI, 965- 966.

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