Autonomous parsing of behavior in a multi-agent setting

D. Vanderelst, E. Barakova

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

1 Citation (Scopus)

Abstract

Imitation learning is a promising route to instruct robotic multi-agent systems. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman Net) can be used to extract meaningful chunks from a continuous sequence of observed actions. Results suggest that, even in spite of the high level of task specific noise, Elman nets can be used for isolating re-occurring action patterns in robots. Limitations and future directions are discussed.

Original languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing - ICAISC 2008 - 9th International Conference, Zakopane, Poland, June 22-26. Proceedings
EditorsL. Rutkowski, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada
Pages1198-1209
Number of pages12
DOIs
Publication statusPublished - 4 Aug 2008
Event9th International conference on Artificial Intelligence and Soft Computing (ICAISC 2008) - Zakopane, Poland
Duration: 22 Jun 200826 Jun 2008
Conference number: 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5097 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International conference on Artificial Intelligence and Soft Computing (ICAISC 2008)
Abbreviated titleICAISC 2008
CountryPoland
CityZakopane
Period22/06/0826/06/08

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  • Cite this

    Vanderelst, D., & Barakova, E. (2008). Autonomous parsing of behavior in a multi-agent setting. In L. Rutkowski, R. Tadeusiewicz, L. A. Zadeh, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing - ICAISC 2008 - 9th International Conference, Zakopane, Poland, June 22-26. Proceedings (pp. 1198-1209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5097 LNAI). https://doi.org/10.1007/978-3-540-69731-2_112