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Related Experiment Videos

Explainable multi-agent learning for adaptive terrorist network disruption.

Vedat Dogan1, Steven Prestwich2, Barry O'Sullivan2

  • 1Insight Research Ireland Centre for Data Analytics, School of Computer Science & IT, University College Cork, Cork, Ireland. vdogan@ucc.ie.

Scientific Reports
|May 16, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces an explainable multi-agent reinforcement learning (MARL) framework to simulate and analyze adaptive terrorist network disruption. Findings show explainable MARL offers actionable insights for intelligence-led policing in complex environments.

Area of Science:

  • Computational Social Science
  • Artificial Intelligence
  • Network Science

Background:

  • Terrorist networks pose significant challenges due to their adaptive and covert nature.
  • Existing disruption approaches often use static models, missing dynamic and adversarial aspects.
  • Simulating strategic interactions in these networks is crucial for effective counter-crime strategies.

Purpose of the Study:

  • To develop an explainable, game-theoretic multi-agent reinforcement learning (MARL) framework for simulating adaptive terrorist network disruption.
  • To analyze the strategic dynamics between terrorist organizations and law enforcement.
  • To provide a decision-support tool for intelligence-led policing.

Main Methods:

  • Formulated as a partially observable, sequential decision-making process with attacker and defender agents.
Keywords:
Decision Support SystemsExplainable Multi-Agent Reinforcement LearningStrategic Network Disruption

Related Experiment Videos

  • Incorporated domain-informed reward functions capturing network properties.
  • Integrated explainability for interpretable intervention decisions.

    • Main Results:

    • Disruption effectiveness is budget-dependent but shows non-monotonic, network-specific behavior.
    • Outcomes are significantly influenced by attacker-defender strategy interactions.
    • Learned policies generate consistent explanations revealing network vulnerabilities.

    Conclusions:

    • Explainable MARL provides actionable insights into adaptive intervention strategies.
    • The framework serves as a valuable decision-support tool for policing complex networks.
    • Highlights the importance of considering strategic interactions in network disruption.