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  View-based cognitive map learning by an autonomous robot

Mallot, H., Bülthoff, H., Georg, P., Schölkopf, B., & Yasuhara, K. (1995). View-based cognitive map learning by an autonomous robot. In Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN '95) (pp. 381-386). Paris, France: EC2.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-EC58-1 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-EC59-0
Genre: Conference Paper

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 Creators:
Mallot, HA1, Author           
Bülthoff, HH1, Author           
Georg, P1, Author           
Schölkopf, B2, Author           
Yasuhara, K1, Author           
Fogelman-Soulié, F., Editor
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: This paper presents a view-based approach to map learning and navigation in mazes. By means of graph theory we have shown that the view-graph is a sufficient representation for map behaviour such as path planning. A neural network for unsupervised learning of the view-graph from sequences of views is constructed. We use a modified Kohonen (1988) learning rule that transforms temporal sequence (rather than featural similarity) into connectedness. In the main part of the paper, we present a robot implementation of the scheme. The results show that the proposed network is able to support map behaviour in simple environments.

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 Dates: 1995-10
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 2-910085-18-X
BibTex Citekey: 668
 Degree: -

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Title: Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN '95)
Place of Event: Paris, France
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Title: Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN '95)
Source Genre: Proceedings
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Publ. Info: Paris, France : EC2
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 381 - 386 Identifier: -