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SMILE: Sound Multi-agent Incremental LEarning

Published: 14 May 2007 Publication History

Abstract

This article deals with the problem of collaborative learning in a multi-agent system. Here each agent can update incrementally its beliefs B (the concept representation) so that it is in a way kept consistent with the whole set of information K (the examples) that he has received from the environment or other agents. We extend this notion of consistency (or soundness) to the whole MAS and discuss how to obtain that, at any moment, a same consistent concept representation is present in each agent. The corresponding protocol is applied to supervised concept learning. The resulting method SMILE (standing for Sound Multi-agent Incremental LEarning) is described and experimented here. Surprisingly some difficult boolean formulas are better learned, given the same learning set, by a Multi agent system than by a single agent.

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    cover image ACM Other conferences
    AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
    May 2007
    1585 pages
    ISBN:9788190426275
    DOI:10.1145/1329125
    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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    Published: 14 May 2007

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    • (2017)WavesProceedings of the International Conference on Web Intelligence10.1145/3106426.3106544(314-321)Online publication date: 23-Aug-2017
    • (2017)Effect of Network Topology on Neighbourhood-Aided Collective LearningComputational Collective Intelligence10.1007/978-3-319-67074-4_20(202-211)Online publication date: 7-Sep-2017
    • (2016)Collaborative Decision in Multi-Agent Learning of Action Models2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2016.0103(640-647)Online publication date: Nov-2016
    • (2016)Collaborative Online Learning of an Action ModelSolving Large Scale Learning Tasks. Challenges and Algorithms10.1007/978-3-319-41706-6_16(300-319)Online publication date: 3-Jul-2016
    • (2014)A consistency based approach of action model learning in a community of agentsProceedings of the 2014 international conference on Autonomous agents and multi-agent systems10.5555/2615731.2616060(1557-1558)Online publication date: 5-May-2014
    • (2014)DESIGNING PROTOCOLS FOR ABDUCTIVE HYPOTHESIS REFINEMENT IN DYNAMIC MULTIAGENT ENVIRONMENTSComputational Intelligence10.1111/j.1467-8640.2012.00468.x30:2(362-401)Online publication date: 1-May-2014
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    • (2010)Learning better togetherProceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence10.5555/1860967.1860985(85-90)Online publication date: 4-Aug-2010
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