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A review of machine learning for automated planning

Published online by Cambridge University Press:  12 November 2012

Sergio Jiménez ,
Tomás De La Rosa ,
Susana Fernández ,
Fernando Fernández  and
Daniel Borrajo
Sergio Jiménez
Affiliation:
Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad, 30. Leganés (Madrid), Spain; e-mail: [email protected]
Tomás De La Rosa
Affiliation:
Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad, 30. Leganés (Madrid), Spain; e-mail: [email protected]
Susana Fernández
Affiliation:
Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad, 30. Leganés (Madrid), Spain; e-mail: [email protected]
Fernando Fernández
Affiliation:
Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad, 30. Leganés (Madrid), Spain; e-mail: [email protected]
Daniel Borrajo
Affiliation:
Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad, 30. Leganés (Madrid), Spain; e-mail: [email protected]
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Abstract

Recent discoveries in automated planning are broadening the scope of planners, from toy problems to real applications. However, applying automated planners to real-world problems is far from simple. On the one hand, the definition of accurate action models for planning is still a bottleneck. On the other hand, off-the-shelf planners fail to scale-up and to provide good solutions in many domains. In these problematic domains, planners can exploit domain-specific control knowledge to improve their performance in terms of both speed and quality of the solutions. However, manual definition of control knowledge is quite difficult. This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge. It has been organized according to the target of the learning process: automatic definition of planning action models and automatic definition of planning control knowledge. In addition, the paper reviews the advances in the related field of reinforcement learning.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Issue 4 , December 2012 , pp. 433 - 467
Copyright
Copyright © Cambridge University Press 2012

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