Contemporary cost-based goal-recognition assumes rationality: that observed behaviour is more or less optimal. Probabilistic goal recognition systems, however, explicitly depend on some degree of sub-optimality to generate probability distributions. We show that, even when an observed agent is only slightly irrational (sub-optimal), state-of-the-art systems produce counter-intuitive results (though these may only become noticeable when the agent is highly irrational). We provide a definition of rationality appropriate to situations where the ground truth is unknown, define a rationality measure (RM) that quantifies an agent's expected degree of sub-optimality, and define an innovative self-modulating probability distribution formula for goal recognition. Our formula recognises sub-optimality and adjusts its level of confidence accordingly, thereby handling irrationality—and rationality—in an intuitive, principled manner. Building on that formula, moreover, we strengthen a previously published result, showing that “single-observation” recognition in the path-planning domain achieves identical results to more computationally expensive techniques, where previously we claimed only to achieve equivalent rankings though values differed.

当代的基于成本的目标识别假设是合理的：观察到的行为或多或少是最优的。然而，概率目标识别系统明显依赖于某种程度的次优性来生成概率分布。我们表明，即使当观察到的特工只是稍微不合理（次优）时，最新的系统也会产生违反直觉的结果（尽管当特工非常不合理时，这些结果可能才变得明显）。我们提供了适用于基本事实未知的情况的合理性定义，定义了对代理预期的次优程度进行量化的合理性度量（RM），并定义了用于目标识别的创新型自调节概率分布公式。我们的公式可识别次优状态，并相应地调整其置信度，从而以一种直观，有原则的方式处理非理性和理性。此外，在此公式的基础上，我们加强了以前发表的结果，表明在路径规划领域中的“单观察”识别与更昂贵的计算技术可以达到相同的结果，以前我们声称尽管值不同，但只能获得同等的排名。