A multi-agent reinforcement learning-based framework for forecasting terrorist collaboration and predicting future alliances

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dc.contributor.authorDogan, Vedat
dc.contributor.authorPrestwich, Steven David
dc.contributor.authorO'Sullivan, Barry
dc.contributor.editorAn, Aijun
dc.contributor.editorCuzzocrea, Alfredo
dc.contributor.editorHu, Hongxin
dc.contributor.funderInsight Research Ireland Centre for Data Analytics
dc.contributor.funderTaighde Éireann - Research Ireland
dc.date.accessioned2026-07-01T14:50:02Z
dc.date.available2026-07-01T14:50:02Z
dc.date.issued2026-02-03
dc.description.abstractTerrorist activity has increased over the years, leading to the rise of new criminal organizations, the persistence of incidents, and increased collaboration and coordination among criminal entities. This study proposes a framework based on multi-agent reinforcement learning (MARL) to forecast terrorism collaboration dynamics from time-series data and predict future collaborations. Firstly, we retrieve data from the Global Terrorist Database for numerous countries and construct a terrorist collaboration network. Subsequently, we employ the cumulative time series data to construct cumulative temporal graphs, thereby facilitating the observation of the evolution of collaboration over time. Then, we design a reward function that quantifies the lethality of terrorist groups, the benefits of collaborations, the group’s role in the network and the effectiveness of the partnership. Finally, we use the learned parameters to generate unobserved terrorist collaboration networks and, therefore, to predict the future potential collaborations for terrorist groups. The research findings demonstrate that the MARL approach exhibits superior forecasting performance in predicting terrorist collaboration networks. Future research endeavours should explore the potential of AI in countering terrorist activities.en
dc.description.sponsorshipResearch Ireland| 12-RC-2289-P2
dc.description.versionAccepted Version
dc.format.extent17
dc.format.mimetypeapplication/pdfen
dc.identifier.authororcidDogan, Vedat§0000-0002-3807-5425
dc.identifier.authororcidPrestwich, Steven David
dc.identifier.authororcidO'Sullivan, Barry§0000-0002-0090-2085
dc.identifier.authororcidAn, Aijun
dc.identifier.authororcidCuzzocrea, Alfredo
dc.identifier.authororcidHu, Hongxin
dc.identifier.citationDogan, V, Prestwich, S D & O'Sullivan, B 2026, A multi-agent reinforcement learning-based framework for forecasting terrorist collaboration and predicting future alliances. in A An, A Cuzzocrea & H Hu (eds), Social Networks Analysis and Mining - 17th International Conference, ASONAM 2025, Proceedings. vol. 16323, Lecture Notes in Computer Science, vol. 16323 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 146-162, 17th International Conference on Social Networks Analysis and Mining, ASONAM 2025, Niagara Falls, Canada, 25/08/25. https://doi.org/10.1007/978-3-032-13821-7_14
dc.identifier.doi10.1007/978-3-032-13821-7_14
dc.identifier.endpage162
dc.identifier.isbn978-3-032-13820-0
dc.identifier.isbn978-3-032-13821-7
dc.identifier.issn0302-9743
dc.identifier.otherORCID: /0000-0002-3807-5425/work/219410386
dc.identifier.startpage146
dc.identifier.urihttps://hdl.handle.net/10468/18987
dc.identifier.volume16323
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.urihttps://www.scopus.com/pages/publications/105029868542
dc.rights© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectCounter-terrorism
dc.subjectForecasting terrorist Collaboration
dc.subjectMulti-agent reinforcement learning
dc.subjectPredictive models
dc.subject[ComputerScience]
dc.titleA multi-agent reinforcement learning-based framework for forecasting terrorist collaboration and predicting future alliancesen
dc.typeConference item
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