Abstract
To scale-up to real-world problems, planning systems must be able to replan in order to deal with changes in problem context. In this paper we describe hierarchical task network and operator-based replanning techniques which allow adaptation of a previous plan to account for problems associated with executing plans in real-world domains with uncertainty, concurrency, changing objectives. We focus on replanning which preserves elements of the original plan in order to use more reliable domain knowledge and to facilitate user understanding of produced plans. We present empirical results documenting the effectiveness of these techniques in a NASA antenna operations application.
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These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
2 This paper describes work performed by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
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© 1997 Springer-Verlag Berlin Heidelberg
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Wang, X., Chien, S. (1997). Replanning using hierarchical task network and operator-based planning. In: Steel, S., Alami, R. (eds) Recent Advances in AI Planning. ECP 1997. Lecture Notes in Computer Science, vol 1348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63912-8_104
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DOI: https://doi.org/10.1007/3-540-63912-8_104
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