Abstract
Plan recognition is an active research area in automatic reasoning, as well as a promising approach to engineering interfaces that can exploit models of user's plans and goals. Much research in the field has focused on the development of plan recognition algorithms to support particular user/system interactions, such as found in naturally occurring dialogues. However, two questions have typically remained unexamined: 1) exactly what kind of interface tasks can knowledge of a user's plans be used to support across communication modalities, and 2) how can such tasks in turn constrain development of plan recognition algorithms? In this paper we present a concrete exploration of these issues. In particular, we provide an assessment of plan recognition, with respect to the use of plan recognition in enhancing user interfaces. We clarify how use of a user model containing plans makes interfaces more intelligent and interactive (by providing an intelligent assistant that supports such tasks as advice generation, task completion, context-sensitive responses, error detection and recovery). We then show how interface tasks in turn provide constraints that must be satisfied in order for any plan recognizer to construct and represent a plan in ways that efficiently support these tasks. Finally, we survey how interfaces are fundamentally limited by current plan recognition approaches, and use these limitations to identify and motivate current research. Our research is developed in the context of CHECS, a plan-based design interface.
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Dr. Bradley A. Goodman is a Group Leader for AI Systems in the Computing Research and Technology Department at The MITRE Corporation. Dr. Goodman received his B.S. degree in Mathematics from Carnegie-Mellon University and his M.S. and Ph.D. degrees in Computer Science from the University of Illinois at Urbana-Champaign. Dr. Goodman was a Senior Scientist in the Speech and Natural Language Department at Bolt Beranek and Newman Inc. from 1980 tot 1990. He has worked in several areas of artificial intelligence, including natural language processing, intelligent tutoring systems, and intelligent interfaces. A thrust in all of his research is the use of discourse processing to further robust communication.
Dr. Diane J. Litman is Assistant Professor of Computer Science at Columbia University. Dr. Litman received her A.B. degree in Mathematics and in Computer Science from the College of William and Mary and her M.S. and Ph.D. degrees in Computer Science from the University of Rochester. From 1985–1990, Dr. Litman was a Member of Technical Staff, Artificial Intelligence Principles Research Department, AT&T Bell Laboratories. Dr. Litman has worked in several areas of artificial intelligence, including spoken and written discourse processing, plan recognition, and knowledge representation. Her contribution is based on work on applying plan recognition conducted while at AT&T Bell Laboratories, as well as experiences gained from her Ph.D. work in plan-based discourse understanding.
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Goodman, B.A., Litman, D.J. On the interaction between plan recognition and intelligent interfaces. User Model User-Adap Inter 2, 83–115 (1992). https://doi.org/10.1007/BF01101860
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DOI: https://doi.org/10.1007/BF01101860