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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 > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
    Related URLs:
    Depositing User: Lukas Chrpa
    Date Deposited: 14 Feb 2012 12:32
    Last Modified: 14 Feb 2012 12:32
    URI: http://eprints.hud.ac.uk/id/eprint/12170

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