venkatesh-negotiatingagents-1996.pdf (548.29 kB)
Negotiating agents that learn about others’ preferences
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 situation where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).
History
Event
Workshop on Adaptation, Coevolution and Learning in Multiagent Systems (1996 : Stanford, Calif.)Pagination
16 - 21Publisher
AAAI PressLocation
Stanford, Calif.Place of publication
Menlo Park, Calif.Start date
1996-03-25End date
1996-03-27ISBN-13
9780929280998ISBN-10
0929280997Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
1996, AAAITitle of proceedings
Adaptation, coevolution and learning in multiagent systems : papers from the 1996 AAAI SymposiumUsage metrics
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