  Goal recognition is a fundamental cognitive process that enables individuals
to infer intentions based on available cues. Current goal recognition
algorithms often take only observed actions as input, but here we use a
Bayesian framework to explore the role of actions, timing, and goal solvability
in goal recognition. We analyze human responses to goal-recognition problems in
the Sokoban domain, and find that actions are assigned most importance, but
that timing and solvability also influence goal recognition in some cases,
especially when actions are uninformative. We leverage these findings to
develop a goal recognition model that matches human inferences more closely
than do existing algorithms. Our work provides new insight into human goal
recognition and takes a step towards more human-like AI models.
