Making friends on the fly: Cooperating with new teammates

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Machine Theory of Mind (2018, background): “Such models will be important for value alignment (Hadﬁeld-Menell et al., 2016) and ﬂexible cooperation (Nowak, 2006; Kleiman-Weiner et al., 2016; Barrett et al., 2017; Kris Cao), and will likely be an ingredient in future machines’ ethical decision making (Churchland, 1996).”
Multi-Agent Generative Adversarial Imitation Learning (2018, background): “The most common case is fully cooperative agents [39], where the challenges mainly lie in other factors, such as unknown role assignments [40], scalability to swarm systems [41] and agents with partial observations [42].”
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning (2020, methodology): “A predominant approach in ad hoc teamwork is to compute Bayesian posteriors over defined teammate types and utilizing the posteriors in reinforcement learning methods (such as Monte Carlo Tree Search) to obtain optimal responses (Barrett et al., 2017; Albrecht et al., 2016).”