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A recent approach based on Bayesian inverse planning for the "theory of mind"has shown good performance in modeling human cognition. However, perfectinverse planning differs from human cognition during one kind of complex tasksdue to human bounded rationality. One example is an environment in which thereare many available plans for achieving a specific goal. We propose a "planpredictability oriented model" as a model of inferring other peoples' goals incomplex environments. This model adds the bias that people prefer predictableplans. This bias is calculated with simple plan prediction. We tested thismodel with a behavioral experiment in which humans observed the partial path ofgoal-directed actions. Our model had a higher correlation with human inference.We also confirmed the robustness of our model with complex tasks and determinedthat it can be improved by taking account of individual differences in "boundedrationality".

Modeling Human Inference of Others' Intentions in Complex Situations with Plan Predictability Bias