Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability
This work addresses goal recognition for AI systems to better emulate human cognitive processes, though it is incremental as it builds on existing Bayesian frameworks with new empirical insights.
The study tackled the problem of goal recognition by investigating how actions, timing, and goal solvability influence human inferences, finding that actions are most important but timing and solvability also play roles, and developed a Bayesian model that matches human inferences more closely than existing algorithms.
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.