It Is Among Us: Identifying Adversaries in Ad-hoc Domains Using Q-valued Bayesian Estimations

do Carmo Alves, Matheus Aparecido and Varma, Amokh and Soriano Marcolino, Leandro and Elkhatib, Yehia (2023) It Is Among Us: Identifying Adversaries in Ad-hoc Domains Using Q-valued Bayesian Estimations. In: Proc. of the 23rd International Conference on Autonomous Agents and Multiagent Systems :. IFAAMAS, NZL. (In Press)

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Abstract

Ad-hoc teamwork models are crucial for solving distributed tasks in environments with unknown teammates. In order to improve performance, agents may collaborate in the same environment, trusting each other and exchanging information. However, what happens if there is an impostor among the team? In this paper, we present BAE, a novel and efficient framework for online planning and estimation within ad-hoc teamwork domains where there is an adversarial agent disguised as a teammate. Our approach considers the identification of the impostor through a process we term ``Q-valued Bayesian Estimation''. BAE can identify the adversary at the same time the agent performs ad-hoc estimation in order to improve coordination. Our results show that BAE has superior accuracy and faster reasoning capabilities in comparison to the state-of-the-art.