When inferring the goals that others are trying to achieve, people
intuitively understand that others might make mistakes along the way. This is
crucial for activities such as teaching, offering assistance, and deciding
between blame or forgiveness. However, Bayesian models of theory of mind have
generally not accounted for these mistakes, instead modeling agents as mostly
optimal in achieving their goals. As a result, they are unable to explain
phenomena like locking oneself out of one's house, or losing a game of chess.
Here, we extend the Bayesian Theory of Mind framework to model boundedly
rational agents who may have mistaken goals, plans, and actions. We formalize
this by modeling agents as probabilistic programs, where goals may be confused
with semantically similar states, plans may be misguided due to
resource-bounded planning, and actions may be unintended due to execution
errors. We present experiments eliciting human goal inferences in two domains:
(i) a gridworld puzzle with gems locked behind doors, and (ii) a block-stacking
domain. Our model better explains human inferences than alternatives, while
generalizing across domains. These findings indicate the importance of modeling
others as bounded agents, in order to account for the full richness of human
intuitive psychology.Comment: Accepted to CogSci 2021. 6 pages, 5 figures. (Appendix: 1 page, 1
figure