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Combining Learning Techniques for Classical Planning: Macro-operators and Entanglements

Chrpa, Lukáš (2010) Combining Learning Techniques for Classical Planning: Macro-operators and Entanglements. In: Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on. IEEE, Arras, France, pp. 79-86. ISBN 9781424488179

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Abstract

Abstract—Planning techniques recorded a significant progress
during recent years. However, many planning problems remain
still hard and challenging. One of the most promising approaches
is gathering additional knowledge by using learning techniques.
Advantageously, many sorts of knowledge can be encoded back
into planning domains (or problems) and common planning
systems can be applied on them. Macro-operators are well known
sort of knowledge. Macro-operators are operators that represent
a sequence of primitive planning operators that are formalized
like ‘normal‘ planning operators. The other sort of knowledge
consists of pruning unnecessary operators’ instances (actions) by
investigating connections (entanglements) between operators and
initial or goal predicates. In this paper, we will show how we can
put these approaches together. We will of course experimentally
evaluate how the performance of planners is improved. The
experiments showed that combining of these learning techniques
can improve the planning process.

Item Type: Book Chapter
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Lukas Chrpa
Date Deposited: 14 Feb 2012 12:32
Last Modified: 19 Aug 2015 22:54
URI: http://eprints.hud.ac.uk/id/eprint/12170

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