Inference of Other's Minds with Limited Information in Evolutionary Robotics

Abstract

Theory of mind (ToM) is the ability to understand others' mental states (e.g., intentions). Studies on human ToM show that the way we understand others' mental states is very efficient, in the sense that observing only some portion of others' behaviors can lead to successful performance. Recently, ToM has gained interest in robotics to build robots that can engage in complex social interactions. Although it has been shown that robots can infer others' internal states, there has been limited focus on the data utilization of ToM mechanisms in robots. Here we show that robots can infer others' intentions based on limited information by selectively and flexibly using behavioral cues similar to humans. To test such data utilization, we impaired certain parts of an actor robot's behavioral information given to the observer, and compared the observer's performance under each impairment condition. We found that although the observer's performance was not perfect compared to when all information was available, it could infer the actor's mind to a degree if the goal-relevant information was intact. These results demonstrate that, similar to humans, robots can learn to infer others' mental states with limited information.