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Dealing with large sets of stimuli in learning tasks (abstract only)

Published: 01 March 1985 Publication History

Abstract

Most previous AI research in learning has been aimed at achieving competence in sophisticated logical domains such as mathematical reasoning. Such systems usually handle small problems very well. However, many applications of learning may be required to deal with large numbers of inputs (<1000) that are available simultaneously from sensors and other sources. In addition, responses may have to be selected from a large set. Borrowing ideas from stimulus sampling theory, we are working on programs that will handle problems in this domain. Operations include handling probabilistic relations between inputs and behaviors, changing relations through reinforcement, selectively sampling stimuli, and grouping related stimuli.

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  1. Dealing with large sets of stimuli in learning tasks (abstract only)

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    cover image ACM Conferences
    CSC '85: Proceedings of the 1985 ACM thirteenth annual conference on Computer Science
    March 1985
    438 pages
    ISBN:0897911504
    DOI:10.1145/320599
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

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    Published: 01 March 1985

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