Composer: a probabilistic solution to the utility problem in speed-up learning

Jonathan Gratch, Gerald DeJong

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

Abstract

In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the Composer system which embodies a probabilistic solution to the utility problem. We outline the statistical foundations of our approach and compare it against four other approaches which appear in the literature.

Original languageEnglish (US)
Title of host publicationProceedings Tenth National Conference on Artificial Intelligence
PublisherPubl by AAAI
Pages235-240
Number of pages6
ISBN (Print)0262510634
StatePublished - Dec 1 1992
EventProceedings Tenth National Conference on Artificial Intelligence - AAAI-92 - San Jose, CA, USA
Duration: Jul 12 1992Jul 16 1992

Publication series

NameProceedings Tenth National Conference on Artificial Intelligence

Other

OtherProceedings Tenth National Conference on Artificial Intelligence - AAAI-92
CitySan Jose, CA, USA
Period7/12/927/16/92

ASJC Scopus subject areas

  • Engineering(all)

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