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A neurobiologically motivated model for self-organized learning

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Details

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsA Gelbukh, A DeAlbornoz, H TerashimaMarin
PublisherSpringer-Verlag, Berlin
Pages415-424
Number of pages10
Volume3789 LNAI
ISBN (Print)3-540-29896-7
DOIs
Publication statusPublished - 2005
Externally publishedYes
Publication typeA4 Article in a conference publication
Event4th Mexican International Conference on Artificial Intelligence, MICAI 2005 - Monterrey, Mexico
Duration: 14 Nov 200518 Nov 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3789 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference4th Mexican International Conference on Artificial Intelligence, MICAI 2005
CountryMexico
CityMonterrey
Period14/11/0518/11/05

Abstract

We present a neurobiologically motivated model for an agent which generates a representation of its spacial environment by an active exploration. Our main objectives is the introduction of an action-selection mechanism based on the principle of self-reinforcement learning. We introduce the action-selection mechanism under the constraint that the agent receives only information an animal could receive too. Hence, we have to avoid all supervised learning methods which require a teacher. To solve this problem, we define a self-reinforcement signal as qualitative comparison between predicted an perceived stimulus of the agent. The self-reinforcement signal is used to construct internally a self-punishment function and the agent chooses its actions to minimize this function during learning. As a result it turns out that an active action-selection mechanism can improve the performance significantly if the problem to be learned becomes more difficult.