Expecting the unexpected: Goal recognition for rational and irrational agents

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.