Abstract
Intuitively, Automated Planning systems capable of learning from previous experiences should be able to achieve better performance. One way to build on past experiences is to augment domains with macro-operators (i.e. frequent operator sequences). In most existing works, macros are generated from chunks of adjacent operators extracted from a set of plans. Although they provide some interesting results this type of analysis may provide incomplete results. In this paper, we propose ERA, an automatic extraction method for macro-operators from a set of solution plans. Our algorithm is domain and planner independent and can find all macro-operator occurrences even if the operators are non-adjacent. Our method has proven to successfully find macro-operators of different lengths for six different benchmark domains. Also, our experiments highlighted the capital role of considering non-adjacent occurrences in the extraction of macro-operators.
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Notes
- 1.
The operators of the macro can be moved contiguously in the plan without an impact on the final state or without impeding its execution.
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See description in the subsection Identifier construction.
- 3.
Further details can be found at http://www.plg.inf.uc3m.es/ipc2011-learning/Domains.html.
- 4.
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Castellanos-Paez, S., Rombourg, R., Lalanda, P. (2021). ERA: Extracting Planning Macro-Operators from Adjacent and Non-adjacent Sequences. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science(), vol 12280. Springer, Cham. https://doi.org/10.1007/978-3-030-69886-7_3
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