Plan recognition in exploratory domains

This paper describes a challenging plan recognition problem that arises in environments in which agents engage widely in exploratory behavior, and presents new algorithms for effective plan recognition in such settings. In exploratory domains, agents’ actions map onto logs of behavior that include switching between activities, extraneous actions, and mistakes. Flexible pedagogical software, such as the application considered in this paper for statistics education, is a paradigmatic example of such domains, but many other settings exhibit similar characteristics. The paper establishes the task of plan recognition in exploratory domains to be NP-hard and compares several approaches for recognizing plans in these domains, including new heuristic methods that vary the extent to which they employ backtracking, as well as a reduction to constraint-satisfaction problems. The algorithms were empirically evaluated on people’s interaction with flexible, open-ended statistics education sof...

• Type: Journal
• Year: 2012
• Where: AI
• Authors: Ya'akov Gal, Swapna Reddy, Stuart M. Shieber, Andee Rubin, Barbara J. Grosz
• Keywords: AI 2012, Artificial Intelligence, Constraint Satisfaction Problems, Heuristic Approaches, Pedagogical Software