Rational Coordination in Multi-Agent Environments
dc.contributor.author | Gmytrasiewicz, Piotr J. | en_US |
dc.contributor.author | Durfee, Edmund H. | en_US |
dc.date.accessioned | 2006-09-11T14:08:44Z | |
dc.date.available | 2006-09-11T14:08:44Z | |
dc.date.issued | 2000-12 | en_US |
dc.identifier.citation | Gmytrasiewicz, Piotr J.; Durfee, Edmund H.; (2000). "Rational Coordination in Multi-Agent Environments." Autonomous Agents and Multi-Agent Systems 3(4): 319-350. <http://hdl.handle.net/2027.42/44002> | en_US |
dc.identifier.issn | 1387-2532 | en_US |
dc.identifier.issn | 1573-7454 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/44002 | |
dc.description.abstract | We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents, and present a framework that uses this paradigm to determine the choice of coordinated action. We endow an agent with a specialized representation that captures the agent's knowledge about the environment and about the other agents, including its knowledge about their states of knowledge, which can include what they know about the other agents, and so on. This reciprocity leads to a recursive nesting of models. Our framework puts forth a representation for the recursive models and, under the assumption that the nesting of models is finite, uses dynamic programming to solve this representation for the agent's rational choice of action. Using a decision-theoretic approach, our work addresses concerns of agent decision-making about coordinated action in unpredictable situations, without imposing upon agents pre-designed prescriptions, or protocols, about standard rules of interaction. We implemented our method in a number of domains and we show results of coordination among our automated agents, among human-controlled agents, and among our agents coordinating with human-controlled agents. | en_US |
dc.format.extent | 207420 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Computer Science | en_US |
dc.subject.other | Software Engineering/Programming and Operating Systems | en_US |
dc.subject.other | Data Structures, Cryptology and Information Theory | en_US |
dc.subject.other | User Interfaces and Human Computer Interaction | en_US |
dc.subject.other | Artificial Intelligence (Incl. Robotics) | en_US |
dc.subject.other | Coordination | en_US |
dc.subject.other | Rationality | en_US |
dc.subject.other | Decision Theory | en_US |
dc.subject.other | Game Theory | en_US |
dc.subject.other | Agent Modeling | en_US |
dc.title | Rational Coordination in Multi-Agent Environments | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbsecondlevel | Philosophy | en_US |
dc.subject.hlbtoplevel | Humanities | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109 | en_US |
dc.contributor.affiliationother | Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, 76019-0015 | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/44002/1/10458_2004_Article_272540.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/A:1010028119149 | en_US |
dc.identifier.source | Autonomous Agents and Multi-Agent Systems | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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