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Learning other agents' preferences in multiagent negotiation
conference contribution
posted on 1996-01-01, 00:00 authored by H Bui, D Kieronska, Svetha VenkateshSvetha VenkateshIn multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents' preferences via past interactions. Over time, the agents can incrementally update their models of other agents' preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situations where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).
History
Event
National Conference on Artificial Intelligence (13th : 1996 : Portland, Or.)Pagination
114 - 119Publisher
AAAILocation
Portland, Or.Place of publication
Menlo Park, Calif.Start date
1996-08-04End date
1996-08-08ISBN-13
9780262510912ISBN-10
026251091XLanguage
engPublication classification
E1.1 Full written paper - refereedCopyright notice
1996, AAAITitle of proceedings
AAAI-96 : Proceedings of the 13th National Conference on Artificial IntelligenceUsage metrics
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