Learning agents with evolving hypothesis classes

Peter Sunehag, Marcus Hutter

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Citations (Scopus)

    Abstract

    It has recently been shown that a Bayesian agent with a universal hypothesis class resolves most induction problems discussed in the philosophy of science. These ideal agents are, however, neither practical nor a good model for how real science works. We here introduce a framework for learning based on implicit beliefs over all possible hypotheses and limited sets of explicit theories sampled from an implicit distribution represented only by the process by which it generates new hypotheses. We address the questions of how to act based on a limited set of theories as well as what an ideal sampling process should be like. Finally, we discuss topics in philosophy of science and cognitive science from the perspective of this framework.

    Original languageEnglish
    Title of host publicationArtificial General Intelligence - 6th International Conference, AGI 2013, Proceedings
    Pages150-159
    Number of pages10
    DOIs
    Publication statusPublished - 2013
    Event6th International Conference on Artificial General Intelligence, AGI 2013 - Beijing, China
    Duration: 31 Jul 20133 Aug 2013

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7999 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference6th International Conference on Artificial General Intelligence, AGI 2013
    Country/TerritoryChina
    CityBeijing
    Period31/07/133/08/13

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