From Global Selective Perception to Local Selective Perception

This paper presents a reinforcement learning algorithm used to allocate tasks to agents in an uncertain real-time environment. In such environment, tasks have to be analyzed and allocated really fast for the multiagent system to be effective. To analyze those tasks, described by a lot of attributes,...

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Published inInternational Conference on Autonomous Agents: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3; 19-23 July 2004 pp. 1352 - 1353
Main Authors Paquet, Sebastien, Bernier, Nicolas, Chaib-draa, Brahim
Format Conference Proceeding
LanguageEnglish
Published Washington, DC, USA IEEE Computer Society 19.07.2004
IEEE
SeriesACM Conferences
Subjects
Algorithm design and analysis
Buildings
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems
Computing methodologies > Artificial intelligence > Planning and scheduling
Computing methodologies > Machine learning
Decision trees
Earthquakes
Fires
Learning
Multiagent systems
Roads
Testing
Tree data structures
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Summary:This paper presents a reinforcement learning algorithm used to allocate tasks to agents in an uncertain real-time environment. In such environment, tasks have to be analyzed and allocated really fast for the multiagent system to be effective. To analyze those tasks, described by a lot of attributes, we have used a selective perception technique to enable agents to narrow down the description of each task, enabling the reinforcement learning algorithm to work on a problem with a reasonable number of possible states.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781581138641
1581138644
DOI:10.5555/1018411.1018939