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dc.contributor.authorLukasiewicz, T
dc.contributor.authorMalizia, E
dc.date.accessioned2019-01-02T11:10:17Z
dc.date.issued2019-01-31
dc.description.abstractAggregating preferences over combinatorial domains has many applications in artificial intelligence (AI). Given the inherent exponential nature of preferences over combinatorial domains, compact representation languages are needed to represent them, and (m)CP-nets are among the most studied ones. Sequential and global voting are two different ways of aggregating preferences represented via CP-nets. In sequential voting, agents’ preferences are aggregated feature-by-feature. For this reason, sequential voting may exhibit voting paradoxes, i.e., the possibility to select sub-optimal outcomes when preferences have specific feature dependencies. To avoid paradoxes in sequential voting, one has often assumed the (quite) restrictive constraint of O-legality, which imposes a shared common topological order among all the agents’ CP-nets. On the contrary, in global voting, CP-nets are considered as a whole during the preference aggregation process. For this reason, global voting is immune from the voting paradoxes of sequential voting, and hence there is no need to impose restrictions over the CP-nets’ structure when preferences are aggregated via global voting. Sequential voting over O-legal CP-nets received much attention, and O-legality of CP-nets has often been required in other studies. On the other hand, global voting over non-O-legal CP-nets has not carefully been analyzed, despite it was explicitly stated in the literature that a theoretical comparison between global and sequential voting was highly promising and a precise complexity analysis for global voting has been asked for multiple times. In quite a few works, only very partial results on the complexity of global voting over CP-nets have been given. In this paper, we start to fill this gap by carrying out a thorough computational complexity analysis of global voting tasks, for Pareto and majority voting, over not necessarily O-legal acyclic binary polynomially connected (m)CP-nets. We show that all these problems belong to various levels of the polynomial hierarchy, and some of them are even in P or LOGSPACE. Our results are a notable achievement, given that the previously known upper bound for most of these problems was the complexity class EXPTIME. We provide various exact complexity results showing tight lower bounds and matching upper bounds for problems that (up to now) did not have any explicit non-obvious lower bound.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citationVol. 272, pp. 101-142.en_GB
dc.identifier.doi10.1016/j.artint.2018.12.010
dc.identifier.grantnumberEP/J008346/1en_GB
dc.identifier.grantnumberEP/L012138/1en_GB
dc.identifier.grantnumberEP/M025268/1en_GB
dc.identifier.grantnumberEP/N510129/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/35288
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights.embargoreasonUnder embargo until 31 January 2020 in compliance with publisher policy.
dc.rights© 2019 Elsevier B.V. All rights reserved.
dc.subjectPreferencesen_GB
dc.subjectCombinatorial preferencesen_GB
dc.subjectPreference aggregationen_GB
dc.subjectCP-netsen_GB
dc.subjectmCP-netsen_GB
dc.subjectVotingen_GB
dc.subjectPareto votingen_GB
dc.subjectMajority votingen_GB
dc.subjectComputational complexityen_GB
dc.subjectPolynomial hierarchyen_GB
dc.titleComplexity Results for Preference Aggregation over (m)CP-nets: Pareto and Majority Votingen_GB
dc.typeArticleen_GB
dc.date.available2019-01-02T11:10:17Z
dc.identifier.issn0004-3702
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.en_GB
dc.identifier.journalArtificial Intelligenceen_GB
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/ en_GB
dcterms.dateAccepted2018-12-21
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2018-12-21
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2018-12-26T10:44:50Z
refterms.versionFCDAM
refterms.panelBen_GB


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© 2019 Elsevier B.V. All rights reserved.
Except where otherwise noted, this item's licence is described as © 2019 Elsevier B.V. All rights reserved.