Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability

TL;DR

This paper uses a Bayesian approach to understand how humans recognize goals by analyzing actions, timing, and solvability, leading to a model that better mimics human inference in goal recognition tasks.

Contribution

It introduces a Bayesian framework for goal recognition that incorporates timing and solvability, improving alignment with human reasoning over existing methods.

Findings

Actions are most influential in goal recognition

Timing and solvability affect recognition when actions are uninformative

The proposed model better matches human inferences

Abstract

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.