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

Dogan, Vedat; Prestwich, Steven David; O'Sullivan, Barry

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

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Files

_Camera_ready_for_ASONAM2025_MARL_Based_Forecasting_Terrorist_Collaboration.pdf(5.88 MB)

Date

2026-02-03

Authors

Dogan, Vedat

Prestwich, Steven David

O'Sullivan, Barry

Publisher

Springer Science and Business Media Deutschland GmbH

Published Version

10.1007/978-3-032-13821-7_14

Abstract

Terrorist 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.

Keywords

Counter-terrorism , Forecasting terrorist Collaboration , Multi-agent reinforcement learning , Predictive models , [ComputerScience]

Citation

Dogan, 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