Counterterrorism planning by multi-objective multi-agent reinforcement learning

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dc.check.date2026-08-02en
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisheren
dc.contributor.authorPrestwich, Stevenen
dc.contributor.authorDogan, Vedaten
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.funderHorizon 2020en
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2025-09-12T11:36:38Z
dc.date.available2025-09-12T11:36:38Z
dc.date.issued2025en
dc.description.abstractIn areas including counterterrorism, security, diplomacy and supply chain optimisation, an analyst must make decisions under assumptions about the risks posed by an adversary. Research fields including operations research, decision theory, game theory, influence diagrams and adversarial risk analysis provide a rich variety of methods to model and solve such problems. Reinforcement learning (RL) is also an approach to sequential decision making that has been applied to specific problems involving risk. We propose multi-objective multi-agent RL (MOMARL) as a general-purpose approach to risk analysis. Using a MOMARL solver we model and solve variants of a problem in counterterrorism, including a notoriously difficult problem class: pessimistic bilevel optimisation under uncertainty.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPrestwich, S., Dogan, V. and O'Sullivan, B. (2025) 'Counterterrorism planning by multi-objective multi-agent reinforcement learning', in Arabnia, H. R., Deligiannidis, L., Shenavarmasouleh, F., Amirian, S., Ghareh Mohammadi, F. (eds) Computational Science and Computational Intelligence. CSCI 2024. Communications in Computer and Information Science, 2510, pp. 51-63. Cham: Springer. https://doi.org/10.1007/978-3-031-94956-2_4en
dc.identifier.doi10.1007/978-3-031-94956-2_4en
dc.identifier.endpage63en
dc.identifier.isbn9783031949555en
dc.identifier.isbn9783031949562en
dc.identifier.issn1865-0929en
dc.identifier.issn1865-0937en
dc.identifier.journaltitleCommunications in Computer and Information Scienceen
dc.identifier.startpage51en
dc.identifier.urihttps://hdl.handle.net/10468/17864
dc.identifier.volume2510en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.ispartofCommunications in Computer and Information Scienceen
dc.relation.ispartofComputational Science and Computational Intelligence 11th International Conference, CSCI 2024, Las Vegas, NV, USA, December 11–13, 2024, Proceedings, Part Xen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/952215/EU/Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization/TAILORen
dc.rights© 2025, The Authors, under exclusive license to Springer Nature Switzerland AG. This version of the paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-94956-2_4en
dc.subjectCounterterrorismen
dc.subjectReinforcement learningen
dc.subjectMulti-objectiveen
dc.subjectMulti-agenten
dc.titleCounterterrorism planning by multi-objective multi-agent reinforcement learningen
dc.typeConference itemen
dc.typebook-chapteren
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