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.

翻译：目标识别是一种基本的认知过程，使个体能够基于可用线索推断意图。当前的目标识别算法通常仅以观察到的动作为输入，但本文采用贝叶斯框架，探究动作、时序与目标可解性在目标识别中的作用。我们分析了人类在推箱子领域对目标识别问题的响应，发现动作被赋予最大权重，但时序与可解性在某些情况下也会影响目标识别，尤其是在动作信息量不足时。基于这些发现，我们开发了一种比现有算法更贴近人类推断的目标识别模型。本研究为人类目标识别提供了新见解，并向更具类人特性的AI模型迈出了一步。