Online Goal Recognition through Mirroring: Humans and Agents

Goal recognition is the problem of inferring the (unobserved) goal of an agent, based on a sequence of its observed actions. Inspired by mirroring processes in human brains, we advocate goal mirroring, an online recognition approach that uses a black-box planner to generate recognition hypotheses. This approach avoids the prevalent assumption in current approaches, which rely on a dedicated plan library, representing all known plans to achieve known goals. Such methods are inherently limited to the knowledge represented in the library. In this paper, we (i) describe a novel online goal mirroring algorithm for continuous spaces; (ii) evaluate a novel heuristic for choosing between competing recognition hypotheses; (iii) contrast machine and human recognition in two challenging domains, revealing insights as to human capabilities; and (iv) compare mirroring to library-based methods.