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dc.contributor.authorSaffidine, Abdallah
dc.contributor.authorSchwarzentruber, François
HAL ID: 15714
ORCID: 0000-0002-1228-4333
dc.contributor.authorZanuttini, Bruno
dc.date.accessioned2020-09-29T10:13:11Z
dc.date.available2020-09-29T10:13:11Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20997
dc.language.isoenen
dc.subject.ddc006.3en
dc.titleKnowledge-Based Policies for Qualitative Decentralized POMDPs
dc.typeCommunication / Conférence
dc.description.abstractenQualitative Decentralized Partially Observable Markov Decision Problems (QDec-POMDPs) constitute a very general class of decision problems. They involve multiple agents, decentralized execution, sequential decision, partial observabil-ity, and uncertainty. Typically, joint policies, which prescribe to each agent an action to take depending on its full history of (local) actions and observations, are huge, which makes it difficult to store them onboard, at execution time, and also hampers the computation of joint plans. We propose and investigate a new representation for joint policies in QDec-POMDPs, which we call Multi-Agent Knowledge-Based Programs (MAKBPs), and which uses epistemic logic for compactly representing conditions on histories. Contrary to standard representations, executing an MAKBP requires reasoning at execution time, but we show that MAKBPs can be exponentially more succinct than any reactive representation.
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01646207
dc.subject.ddclabelIntelligence artificielleen
dc.relation.conftitle32nd AAAI Conference on Artificial Intelligence
dc.relation.confdate2018-02
dc.relation.confcityNew Orleans
dc.relation.confcountryUNITED STATES
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2020-09-30T10:56:41Z


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