Multi-agent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multi-agent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. The problem that this paper seeks to solve is the coordination of decentralized defender agents in the presence of uncertainty while securing targets against an observing adversary. To address this problem, we offer the following novel contributions in this paper: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column generation that utilizes Decentralized Markov Decision Processes (Dec-MDPs) to generate defender strategies that incorporate uncertainty; (iii) New techniques to handle global events (when one or more agents may leave the system) during defender execution; (iv) Heuristics that help scale up in the number of targets and agents to handle real-world scenarios; (v) Exploration of the robustness of randomized pure strategies. The paper opens the door to a potentially new area combining computational game theory and multi-agent teamwork.
Game theory, Dec-MDP, security, stackelberg games, security games
Artificial Intelligence and Robotics | Computer Sciences
Intelligent Systems and Decision Analytics
Multiagent and Grid Systems
SHIEH, Eric; JIANG, Albert Xin; YADAV, Amulya; Pradeep VARAKANTHAM; and TAMBE, Milind.
An extended study on addressing defender teamwork while accounting for uncertainty in attacker defender games using iterative Dec-MDPs. (2016). Multiagent and Grid Systems. 11, (4), 189-226. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3077
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.