A Formal Theory of Plan Recognition Henry A. Kautz* Department of Computer Science University of Rochester, Rochester, NY 14627 TR 215 May 1987 This report reproduces a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. The work was supervised by Dr. James F. Allen. * Current address: AT&T Labs, Room A-257 180 Park Ave., Florham Park, NJ 07932 (973) 360-8310, [email protected] Curriculum Vita Henry Alexander Kautz was born in 1956 in Youngstown, Ohio. After obtaining the highest score statewide on the 1974 New York State Regents Scholarship Examination, he entered the Case Institute of Technology in 1974, and transferred to Cornell University a year later. He received both an B.A. in English and one in mathematics from Cornell in 1978, graduating with highest honors. Mr. Kautz worked as a systems analyst for a year before winning a fellowship to the creative writing program at the Johns Hopkins University in 1979. During this time he wrote two professionally produced plays, and was awarded a M.A. by the Writing Seminars in 1980. That fall Mr. Kautz returned to computer science, enrolling at the University of Toronto in 1980, supported by the Connaught Fellowship for foreign students. He produced his Master's thesis, A First-Order Dynamic Logic for Planning, under the supervision of Professor Ray Perrault, and received an M.S. in Computer Science in 1982. The National Science Foundation selected Mr. Kautz for a three-year fellowship that year, and he returned to the United States, entering the Department of Computer Science at the University of Rochester. Mr. Kautz was a teaching assistant for professors Patrick Hayes and James Allen in the Fall of 1983 and Spring of 1984 respectively, and was a research assistant for James Allen, his thesis advisor, for 1982–1983, and 1984–1987. He held research appointments in the summer of 1983 at BBN Labs in Cambridge, and in the summer of 1984 at SRI International, in Palo Alto, California. Mr. Kautz published papers on a number of topics in Artificial Intelligence during his tenure as a graduate student, including three presented at the 1986 annual convention of the American Association for Artificial Intelligence. Mr. Kautz finished his PhD thesis while employed as a Knowledge Representation Consultant at Bell Laboratories in Murray Hill, New Jersey, in the Spring of 1987. ii Acknowledgments The National Science Foundation helped supported my graduate study under Fellowship Grant No. RCD-8450125 and Research Grant No. DCR-8502481. This work was also supported by the Air Force Systems Command, Rome Air Development Center, Griffiss Air Force Base, New York 13441, and the Air Force Office of Scientific Research, Bolling Air Force Base, Washington, D.C. 20332 under Contract No. F30602-85-C-0008. This contract supports the Northeast Artificial Intelligence Consortium (NAIC). I thank the Xerox Corporation University Grants Program for providing equipment used in the preparation of this paper. I thank my thesis advisor, James Allen, for the countless hours we spent in research meetings and seminars over the years. He was always quick to suggest new ideas, sketch solutions to problems I ran into, and to help separate the wheat from the chaff. The faculty and staff at the University of Rochester provided an exceptionally supportive and collegial environment for graduate students. Thanks for interesting classes and wide ranging discussions go to professors Patrick Hayes, Henry Kyburg, and Jerry Feldman. Giddeon Frieder helped me get my first enjoyable summer job, working for the University of Buffalo Computer Science Department, and was fundamental in my decision to go to graduate school in computer science. Alex Borgida first introduced me to work in A.I. at the University of Toronto, where I was fortunate enough to become the student of Ray Perrault, both of whom taught me to think clearly and rigorously. Mention must be made of Gordy McCall, who hosted the Canadian A.I. Conference in Saskatoon; afterwards I felt like an honorary Canadian citizen for life. I am extremely grateful to Ron Brachman and Bell Laboratories for putting me on the payroll while I finished this document. Time spent with fellow students Leo Hartman, Diane Litman, Josh Tenenberg, Jay Weber, Paul Kates, and Robin Cohen provided food for thought, as well as many fine meals. Jim Mayer taught me everything I know about making pasta, for which I more than forgive his utter skepticism about "artificial intelligence". iii My parents were a constant source of moral and financial support during the many years of graduate study. Thanks for always encouraging me to try for the best. This thesis is dedicated (of course) to Christine, who helped me celebrate the highs and saw me through the lows of working on a PhD. Her constant support, understanding, encouragement, and love have made this not only possible, but truly worthwhile. iv A Formal Theory of Plan Recognition Henry Alexander Kautz Abstract Research in discourse analysis, story understanding, and user modeling for expert systems has shown great interest in plan recognition problems. In a plan recognition problem, one is given a fragmented description of actions performed by one or more agents, and expected to infer the overall plan or scenario which explains those actions. This thesis develops the first formal description of the plan recognition process. Beginning with a reified logic of events, the thesis presents a scheme for hierarchically structuring a library of event types. A semantic basis for non-deductive inference, called "minimum covering entailment", justifies the conclusions that one may draw from a set of observed actions. Minimum covering entailment is defined by delineating the class of models in which the library is complete and the set of unrelated observations is minimized. An equivalent proof theory forms a preliminary basis for mechanizing the theory. Equivalence theorems between the proof and model theories are presented. Minimum covering entailment is related to a formalism for non-monotonic inference known as "circumscription". Finally, the thesis describes a number of algorithms which correctly implement the theory, together with a discussion of their complexity. The theory is applied to a number of examples of plan recognition, in domains ranging from an operating system advisor to the theory of speech acts. The thesis shows how problems of medical diagnosis, a similar kind of non-deductive reasoning, can be cast in the framework, and an example previously solved by a medical expert system is worked out in detail. The analyses provides a firm theoretical foundation for much of what is loosely called "frame based inference", and directly accounts for problems of ambiguity, abstraction, and complex temporal interactions, which were ignored by previous work. The framework can be extended to handle difficult phenomena such v as errors, and can also be restricted in order to improve its computational properties in specialized domains. vi Table of Contents Curriculum Vita............................................................................................................... ii Acknowledgments........................................................................................................... iii Abstract ........................................................................................................................... v List of Tables................................................................................................................... xii List of Figures .................................................................................................................xiii Poèmes humoristiques sur l’AI ....................................................................................... xiv Chapter 1 Introduction ..................................................................................................................... 1 1.1. Motivation .................................................................................................. 1 1.2. Overview of Thesis .................................................................................... 2 1.3. Related Work on Plan Recognition............................................................ 6 1.3.1. Story Understanding..................................................................... 6 1.3.1.1. Psychological Modeling............................................... 6 1.3.1.2. Script Based Systems ................................................... 7 1.3.1.3. Abduction..................................................................... 8 1.3.2. Discourse...................................................................................... 10 1.3.2.1. Allen and Perrault........................................................... 10 1.3.2.2. Extended Discourse....................................................... 11 1.3.2.3. Cohen and Levesque ..................................................... 13 1.3.3. Intelligent Computer Environments............................................. 14 1.3.3.1. The MACSYMA Advisor ............................................ 14 1.3.3.2. A Smart Operating System: Plan Parsing ................... 15 1.4. Related Work on Medical Diagnosis ......................................................... 16 1.4.1. INTERNIST and CADUCEUS................................................... 18 1.4.2. A Set Covering Model................................................................