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dc.contributor.authorParkes, David C.
dc.contributor.authorUngar, Lyle H.
dc.date.accessioned2010-05-18T19:18:41Z
dc.date.issued1997
dc.identifier.citationParkes, David C., and Lyle H. Ungar. 1997. Learning and adaption in multiagent systems. In Multiagent learning: Papers from the 1997 AAAI Workshop: July 28, 1997, Providence, Rhode Island, ed. S. Sen, 47-52. Menlo Park, C.A.: AAAI Press.en_US
dc.identifier.isbn1577350308en_US
dc.identifier.isbn9781577350309en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4101701
dc.description.abstractThe goal of a self-interested agent within a multiagent system is to maximize its utility over time. In a situation of strategic interdependence, where the actions of one agent may a ect the utilities of other agents, the optimal behavior of an agent must be conditioned on the expected behaviors of the other agents in the system. Standard game theory assumes that the rationality and preferences of all the agents is common knowledge: each agent is then able to compute the set of possible equilibria, and if there is a unique equilibrium, choose a best-response to the actions that the other agents will all play. Real agents acting within a multiagent system face multiple problems: the agents may have incomplete information about the preferences and rationality of the other agents in the game, computing the equilibria can be computationally complex, and there might be many equilibria from which to choose. An alternative explanation of the emergence of a stable equilibrium is that it arises as the long-run outcome of a repeated game, in which bounded-rational agents adapt their strategies as they learn about the other agents in the system. We review some possible models of learning for games, and then show the pros and cons of using learning in a particular game, the Compensation Mechanism, a mechanism for the efficient coordination of actions within a multiagent system.en_US
dc.description.sponsorshipEngineering and Applied Sciencesen_US
dc.language.isoen_USen_US
dc.publisherAssociation for the Advancement of Artificial Intelligenceen_US
dc.relation.isversionofhttps://www.aaai.org/Papers/Workshops/1997/WS-97-03/WS97-03-009.pdfen_US
dc.relation.hasversionhttp://www.eecs.harvard.edu/econcs/pubs/learning.pdfen_US
dash.licenseLAA
dc.subjectgame theoryen_US
dc.subjectmechanism designen_US
dc.subjectlearningen_US
dc.titleLearning and Adaption in Multiagent Systemsen_US
dc.typeMonograph or Booken_US
dc.description.versionAccepted Manuscripten_US
dash.depositing.authorParkes, David C.
dc.date.available2010-05-18T19:18:41Z
dash.contributor.affiliatedParkes, David


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