Trading off granularity against complexity in predictive models for complex domains

Ingrid Zukerman, David W Albrecht, Ann E Nicholson, Krystyna Doktor

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    Abstract

    The automated prediction of a user’s interests and requirements is an area of interest to the Artificial Intelligence community. However, current predictive statistical approaches are subject to theoretical and practical limitations which restrict their ability to make useful predictions in domains such as the WWW and computer games that have vast numbers of values for variables of interest. In this paper, we describe an automated abstraction technique which addresses this problem in the context of Dynamic Bayesian Networks. We compare the performance and computational requirements of fine-grained models built with precise variable values with the performance and requirements of a coarse-grained model built with abstracted values. Our results indicate that complex, coarse-grained models offer performance and computational advantages compared to simpler, fine-grained models.
    Original languageEnglish
    Title of host publicationPRICAI 2000 Topics in Artificial Intelligence
    Subtitle of host publication6th Pacific Rim International Conference on Artificial Intelligence Melbourne, Australia, August 28 - September 1,2000 Proceedings
    EditorsRiichiro Mizoguchi, John Slaney
    Place of PublicationBerlin Germany
    PublisherSpringer
    Pages241-251
    Number of pages11
    ISBN (Print)3540679251
    DOIs
    Publication statusPublished - 2000

    Publication series

    NameLecture Notes in Artificial Intelligence
    PublisherSpringer
    Volume1886
    ISSN (Print)0302-9743

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