Our work “It Is Among Us: Identifying Adversaries in Ad-hoc Domains Using Q-valued Bayesian Estimations” was presented at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). That works integrates a Bayesian framework within the Monte Carlo Tree Search algorithm, in order to allow an autonomous agent to identify hidden adversaries in a team. Our paper can be found here, and the source code is on Github.