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dc.contributor.authorRădulescu, Roxana
dc.contributor.authorMannion, Patrick
dc.contributor.authorRoijers, Diederik M.
dc.contributor.authorNowé, Ann
dc.date.accessioned2020-01-07T10:24:55Z
dc.date.issued2019-12-09
dc.identifier.citationRădulescu, Roxana, Mannion, Patrick, Roijers, Diederik M., & Nowé, Ann. (2019). Multi-objective multi-agent decision making: a utility-based analysis and survey. Autonomous Agents and Multi-Agent Systems, 34(1), 10. doi: 10.1007/s10458-019-09433-xen_IE
dc.identifier.issn1573-7454
dc.identifier.urihttp://hdl.handle.net/10379/15673
dc.description.abstractThe majority of multi-agent system implementations aim to optimise agents policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherSpringer Verlagen_IE
dc.relation.ispartofAutonomous Agents and Multi-Agent Systemsen
dc.subjectMulti-agent systemsen_IE
dc.subjectMulti-objective decision makingen_IE
dc.subjectMulti-objective optimisation criteriaen_IE
dc.subjectSolution conceptsen_IE
dc.subjectReinforcement learningen_IE
dc.titleMulti-objective multi-agent decision making: a utility-based analysis and surveyen_IE
dc.typeArticleen_IE
dc.date.updated2020-01-07T10:12:59Z
dc.identifier.doi10.1007/s10458-019-09433-x
dc.local.publishedsourcehttps://doi.org/10.1007/s10458-019-09433-xen_IE
dc.description.peer-reviewedpeer-reviewed
dc.description.embargo2020-12-09
dc.internal.rssid19124103
dc.local.contactPatrick Mannion, Engineering Faculty, Nui Galway. Email: patrick.mannion@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
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