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.
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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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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