A meta-rule approach to flexible plan recognition in dialogue

Although a number of researchers have demonstrated that reasoning on a model of the user's plans and goals is helpful in language understanding and response generation, current models of plan inference cannot handle naturally occurring dialogue. This paper argues that model building from less than ideal dialogues has a great deal in common with processing ill-formed input. It defines well-formedness constraints for information-seeking dialogues and contends that strategies for interpreting ill-formed input can be applied to the problem of modeling the user's plan during an ill-formed dialogue. It presents a meta-rule approach for hypothesizing the cause of dialogue ill-formedness, and describes meta-rules for relaxing the plan inference process and enabling the consideration of alternative hypotheses. The advantages of this approach are that it provides a unified framework for handling both well-formed and ill-formed dialogue, avoids unnatural interpretations when the dialogue is proceeding smoothly, and facilitates a nonmonotonic plan recognition system.Rhonda Eller is a Ph.D. candidate in Computer Science at the University of Delaware. She received her B.S. in Computer Science from Millersville University of Pennsylvania in 1987, and her M.S. degree in the same field from the University of Delaware. Her primary interests lie in the areas of natural language processing, plan recognition, and user modelling. This paper summarizes the current state of her thesis work on repair of an incorrectly inferred plan for a user.Sandra Carberry is an associate professor of computer science at the University of Delaware. Her research interests include discourse understanding, user modeling, planning and plan recognition, and intelligent natural language interfaces. This paper describes work that is part of an ongoing research project to develop a robust model of plan recognition.