Determining Representativeness of Training Plans

A Case of Macro-operators

Lukáš Chrpa, Mauro Vallati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Most learning for planning approaches rely on analysis of training plans. This is especially the case for one of the best-known learning approach: the generation of macrooperators (macros). These plans, usually generated from a very limited set of training tasks, must provide a ground to extract useful knowledge that can be fruitfully exploited by planning engines. In that, training tasks have to be representative of the larger class of planning tasks on which planning engines will then be run. A pivotal question is how such a set of training tasks can be selected.
To address this question, here we introduce a notion of structural similarity of plans. We conjecture that if a class of planning tasks presents structurally similar plans, then a small subset of these tasks is representative enough to learn the same knowledge (macros) as could be learnt from a larger set of tasks of the same class. We have tested our conjecture by focusing on two state-of-the-art macro generation approaches. Our large empirical analysis considering seven state-of-the-art planners, and fourteen benchmark domains from the International Planning Competition, generally confirms our conjecture which can be exploited for selecting small-yet-informative training sets of tasks.
Original languageEnglish
Title of host publication30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018)
PublisherIEEE
Pages488-492
Number of pages5
ISBN (Electronic)9781538674499
ISBN (Print)9781538674505
DOIs
Publication statusPublished - 13 Dec 2018
Event30th IEEE International Conference on Tools with Artificial Intelligence - Volos, Greece
Duration: 5 Nov 20187 Nov 2018
Conference number: 30
http://ictai2018.org/ (Link to Conference Website)

Conference

Conference30th IEEE International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2018
CountryGreece
CityVolos
Period5/11/187/11/18
Internet address

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Macros
Planning
Engines

Cite this

Chrpa, L., & Vallati, M. (2018). Determining Representativeness of Training Plans: A Case of Macro-operators. In 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018) (pp. 488-492). [8576079] IEEE. https://doi.org/10.1109/ICTAI.2018.00081
Chrpa, Lukáš ; Vallati, Mauro. / Determining Representativeness of Training Plans : A Case of Macro-operators. 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018). IEEE, 2018. pp. 488-492
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Chrpa, L & Vallati, M 2018, Determining Representativeness of Training Plans: A Case of Macro-operators. in 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018)., 8576079, IEEE, pp. 488-492, 30th IEEE International Conference on Tools with Artificial Intelligence, Volos, Greece, 5/11/18. https://doi.org/10.1109/ICTAI.2018.00081

Determining Representativeness of Training Plans : A Case of Macro-operators. / Chrpa, Lukáš; Vallati, Mauro.

30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018). IEEE, 2018. p. 488-492 8576079.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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PY - 2018/12/13

Y1 - 2018/12/13

N2 - Most learning for planning approaches rely on analysis of training plans. This is especially the case for one of the best-known learning approach: the generation of macrooperators (macros). These plans, usually generated from a very limited set of training tasks, must provide a ground to extract useful knowledge that can be fruitfully exploited by planning engines. In that, training tasks have to be representative of the larger class of planning tasks on which planning engines will then be run. A pivotal question is how such a set of training tasks can be selected.To address this question, here we introduce a notion of structural similarity of plans. We conjecture that if a class of planning tasks presents structurally similar plans, then a small subset of these tasks is representative enough to learn the same knowledge (macros) as could be learnt from a larger set of tasks of the same class. We have tested our conjecture by focusing on two state-of-the-art macro generation approaches. Our large empirical analysis considering seven state-of-the-art planners, and fourteen benchmark domains from the International Planning Competition, generally confirms our conjecture which can be exploited for selecting small-yet-informative training sets of tasks.

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Chrpa L, Vallati M. Determining Representativeness of Training Plans: A Case of Macro-operators. In 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2018). IEEE. 2018. p. 488-492. 8576079 https://doi.org/10.1109/ICTAI.2018.00081