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Accelerating the Backpropagation Algorithm by Using NMF-Based Method on Deep Neural Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

ERA: Extracting Planning Macro-Operators from Adjacent and Non-adjacent Sequences

Authors : Sandra Castellanos-Paez, Romain Rombourg, Philippe Lalanda

Published in: Knowledge Management and Acquisition for Intelligent Systems

Publisher: Springer International Publishing

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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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Footnotes
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.
 
2
See description in the subsection Identifier construction.
 
Literature
1.
Botea, A., Enzenberger, M., Müller, M., Schaeffer, J.: Macro-FF: improving AI planning with automatically learned macro-operators. J. Artif. Intelli. Res. 24, 581–621 (2005)CrossRef
2.
Botea, A., Müller, M., Schaeffer, J.: Learning partial-order macros from solutions. In: Proceedings of the Fifteenth International Conference on International Conference on Automated Planning and Scheduling, pp. 231–240. AAAI Press (2005)
3.
Chrpa, L., Vallati, M., McCluskey, T.L.: MUM: a technique for maximising the utility of macro-operators by constrained generation and use. In: Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (2014)
4.
Coles, A., Smith, A.: Marvin: a heuristic search planner with online macro-action learning. J. Artif. Intell. Res. 28, 119–156 (2007)CrossRef
5.
6.
Fikes, R., Nilsson, N.: STRIPS: a new approach to the application of theorem proving to problem solving. Artif. Intell. 3–4(2), 189–208 (1971)CrossRef
7.
Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)
8.
Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier, Amsterdam (2004)MATH
9.
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)MATH
10.
Hofmann, T., Niemueller, T., Lakemeyer, G.: Initial results on generating macro actions from a plan database for planning on autonomous mobile robots. In: Twenty-Seventh International Conference on Automated Planning and Scheduling (2017)
11.
Newton, M.A.H., Levine, J.: Implicit learning of macro-actions for planning. In: Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010) (2010)
12.
Metadata
Title
ERA: Extracting Planning Macro-Operators from Adjacent and Non-adjacent Sequences
Authors
Sandra Castellanos-Paez
Romain Rombourg
Philippe Lalanda
Copyright Year
2021
Book
Knowledge Management and Acquisition for Intelligent Systems

Print ISBN: 978-3-030-69885-0

Electronic ISBN: 978-3-030-69886-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-69886-7_3

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