Online Goal Recognition through Mirroring: Humans and Agents

Mor Vered, Gal A. Kaminka, Sivan Biham

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publicationFourth Annual Conference on Advances in Cognitive Systems
EditorsKenneth Forbus, Tom Hinrichs, Carrie Ost
Place of PublicationKirkland WA USA
PublisherCognitive Systems Foundation
Number of pages13
StatePublished - 2016

Bibliographical note

Place of conference:USA


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