TY - GEN
T1 - Counterterrorism Planning by Multi-objective Multi-agent Reinforcement Learning
AU - Prestwich, Steven
AU - Dogan, Vedat
AU - O’Sullivan, Barry
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - In 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.
AB - In 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.
KW - Counterterrorism
KW - Multi-Agent
KW - Multi-Objective
KW - Reinforcement Learning
UR - https://www.scopus.com/pages/publications/105014505956
U2 - 10.1007/978-3-031-94956-2_4
DO - 10.1007/978-3-031-94956-2_4
M3 - Conference proceeding
AN - SCOPUS:105014505956
SN - 9783031949555
T3 - Communications in Computer and Information Science
SP - 51
EP - 63
BT - Computational Science and Computational Intelligence - 11th International Conference, CSCI 2024, Proceedings
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Shenavarmasouleh, Farzan
A2 - Amirian, Soheyla
A2 - Ghareh Mohammadi, Farid
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024
Y2 - 11 December 2024 through 13 December 2024
ER -
