Conference Proceedings

Plan Recognition in Continuous Domains

Gal A Kaminka, Mor Vered, Noa Agmon

THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE | Published : 2018

Abstract

Plan recognition is the task of inferring the plan of an agent, based on an incomplete sequence of its observed actions. Previous formulations of plan recognition commit early to discretizations of the environment and the observed agent's actions. This leads to reduced recognition accuracy. To address this, we first provide a formalization of recognition problems which admits continuous environments, as well as discrete domains. We then show that through mirroring-generalizing plan-recognition by planning-we can apply continuous-world motion planners in plan recognition. We provide formal arguments for the usefulness of mirroring, and empirically evaluate mirroring in more than a thousand re..

View full abstract

University of Melbourne Researchers

Grants

Awarded by ISF


Funding Acknowledgements

We thank Kobi Gal, Miguel Ramirez, and Felipe Meneguzzi for very valuable advice. This research was supported in part by ISF grant #1865/16. Thanks to K. Ushi.