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A framework for co-ordination and learning among teams of agents
conference contribution
posted on 1997-01-01, 00:00 authored by H Bui, Svetha VenkateshSvetha Venkatesh, D KieronskaWe present a framework for team coordination under incomplete information based on the theory of incomplete information games. When the true distribution of the uncertainty involved is not known in advance, we consider a repeated interaction scenario and show that the agents can learn to estimate this distribution and share their estimations with one another. Over time, as the set of agents' estimations become more accurate, the utility they can achieve approaches the optimal utility when the true distribution is known, while the communication requirement for exchanging the estimations among the agents can be kept to a minimal level.
History
Event
Australian Joint Artificial Intelligence Conference (10th : 1997 : Perth, W. A.)Pagination
164 - 178Publisher
SpringerLocation
Perth, W. A.Place of publication
Heidelberg, GermanyPublisher DOI
Start date
1997-11-30End date
1997-12-01ISBN-10
3540647694Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
W Wobcke, M Pagnucco, C ZhangTitle of proceedings
Agents and multi-agent systems : formalisms, methodologies, and applications : based on the AI'97 Workshops on Commonsense Reasoning, Intelligent Agents, and Distributed Artificial IntelligenceUsage metrics
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