Modeling Human Inference of Others' Intentions in Complex Situations
  with Plan Predictability Bias
  A recent approach based on Bayesian inverse planning for the "theory of mind"
has shown good performance in modeling human cognition. However, perfect
inverse planning differs from human cognition during one kind of complex tasks
due to human bounded rationality. One example is an environment in which there
are many available plans for achieving a specific goal. We propose a "plan
predictability oriented model" as a model of inferring other peoples' goals in
complex environments. This model adds the bias that people prefer predictable
plans. This bias is calculated with simple plan prediction. We tested this
model with a behavioral experiment in which humans observed the partial path of
goal-directed actions. Our model had a higher correlation with human inference.
We also confirmed the robustness of our model with complex tasks and determined
that it can be improved by taking account of individual differences in "bounded
rationality".
