Abstract
The satisfiability paradigm has been hitherto applied to planning with only primitive actions. On the other hand, hierarchical task networks have been successfully used in many real world planning applications. Adapting the satisfiability paradigm to hierarchical task network planning, we show how the guidance from the task networks can be used to significantly reduce the sizes of the propositional encodings. We report promising empirical results on various encodings that demonstrate an orders of magnitude reduction in the solving times.
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I thank Subbarao Kambhampati and the anonymous referees of ECP-99 for useful comments on this work. This work was performed while the author was a graduate student at Arizona State University. The college of engineering and applied sciences at Univ. of Wisconsin, Milwaukee provided financial support for attending the conference and presenting the work.
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References
Barrett, A., Weld, D.: Task-Decomposition via plan parsing. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), pp. 1117–1122 (1994)
Blum, A., Furst, M.: Fast planning via planning graph analysis. In: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI (1995)
Ernst, M., Millstein, T., Weld, D.: Automatic SAT compilation of planning problems. In: Proccedings of the International Joint Conference on Artificial Intelligence, IJCAI (1997)
Erol, K.: Hierarchical task network planning: Formalization, Analysis and Implementation, Ph.D thesis, Dept. of computer science, Univ. of Maryland, College Park (1995)
Estlin, T., Chien, S., Wang, X.: An argument for a hybrid HTN/Operator-based approach to planning. In: Steel, S. (ed.) ECP 1997. LNCS, vol. 1348, pp. 184–196. Springer, Heidelberg (1997)
Giunchiglia, E., Massarotto, A., Sebastiani, R.: Act and the rest will follow: Exploiting determinism in planning as satisfiability. In: Proceedings of the National Conference on Artificial Intelligence, AAAI (1998)
Kambhampati, S., Mali, A., Srivastava, B.: Hybrid planning in partially hierarchical domains. In: Proceedings of the National Conference on Artificial Intelligence, AAAI (1998)
Kambhampati, S.: Challenges in bridging the plan synthesis paradigms. In: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI (1997)
Kautz, H., McAllester, D., Selman, B.: Encoding plans in propositional logic. In: Proceedings of the conference on Knowledge Representation & Reasoning, KRR (1996)
Kautz, H., Selman, B.: Pushing the envelope: Planning, Propositional logic and Stochastic search. In: Proceedings of the National Conference on Artificial Intelligence, AAAI (1996)
Kautz, H., Selman, B.: The role of domain-specific knowledge in the planning as satisfiability framework. In: Proceedings of the international conference on Artificial Intelligence Planning Systems, AIPS (1998)
Mali, A.D., Kambhampati, S.: Encoding HTN planning in propositional logic. In: Proceedings of the International Conference on AI Planning Systems, AIPS (1998)
Mali, A.D., Kambhampati, S.: On the utility of causal encodings. In: Proceedings of the National Conference on Artificial Intelligence, AAAI (1999)
Tate, A.: Generating project networks. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 888–893 (1977)
Wilkins, D.: Practical planning: Extending the classical AI planning paradigm. Morgan Kaufmann, San Francisco (1988)
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Mali, A.D. (2000). Hierarchical Task Network Planning as Satisfiability. In: Biundo, S., Fox, M. (eds) Recent Advances in AI Planning. ECP 1999. Lecture Notes in Computer Science(), vol 1809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720246_10
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DOI: https://doi.org/10.1007/10720246_10
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