Proceedings of the Annual Meeting of the Cognitive Science Society

Navigating the social world relies upon the human capacity for mentalizing, or attributing intentions to social agents.Unfortunately, currently available commercial robots still lack such awareness of human intentionality. Building uponrecently-proposed Bayesian models of Theory of Mind (ToM), we propose a ToM model that can handle intention inferencein dynamic, fast-changing environments like a hospital, where staff have to attend to objectives and emergencies as theyarise. Our model infers and maintains a distribution over possible intentions, and uses the posterior predictives to forecastfuture trajectories, which is essential for robot motion planning. We show that our model performs excellently at inferringthe intentions and trajectories of human players controlling a nurse agent in a simulated environment. This work lays thefoundation for robots that can co-work with humans in dynamic, social environments with high-stake goals.