Uncertainty in Artificial Intelligence PDF
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Machine Intelligence and Pattern Recognition Volume 10 Series Editors L.N. KANAL and A. ROSENFELD University of Maryland College Park, Maryland, U.S.A. NORTH-HOLLAND AMSTERDAM · NEW YORK · OXFORD · TOKYO Uncertainty in Artificial Intelligence 5 Edited by Max HENRION Carnegie-Mellon University Pittsburgh, Pennsylvania, U.S.A. and Rockwell International Science Center Palo Alto, California, U.S.A. Ross D. SHACHTER Stanford University Stanford, California, U.S.A. Laveen N. KANAL University of Maryland College Park, Maryland, U.S.A. John F. LEMMER Knowledge Systems Concepts Rome, New York, U.S.A. NORTH-HOLLAND AMSTERDAM · NEW YORK · OXFORD · TOKYO ELSEVIER SCIENCE PUBLISHERS B.V. Sara Burgerhartstraat 25 P.O. Box 211, 1000 AE Amsterdam, The Netherlands Distributors for the United States and Canada: ELSEVIER SCIENCE PUBLISHING COMPANY, INC. 655 Avenue of the Americas New York, N.Y. 10010, U.S.A. ISBN: 0 444 88738 5 (hardbound) ISBN: 0 444 88739 3 (paperback) ©ELSEVIER SCIENCE PUBLISHERS B.V, 1990 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or other- wise, without the prior written permission of the publisher, Elsevier Science Publishers B.V./Physical Sciences and Engineering Division, P.O. Box 103, 1000 AC Amsterdam, The Netherlands. Special regulations for readers in the U.S.A. - This publication has been registered with the Copyright Clearance Center Inc. (CCC), Salem, Massachusetts. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the U.S.A. All other copyright questions, including photocopying outside of the U.S.A., should be referred to the copyright owner, Elsevier Science Publishers B.V, unless otherwise specified. No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, pro- ducts, instructions or ideas contained in the material herein. pp. 271-282: copyright not transferred. Printed in The Netherlands V Preface This collection of papers, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty. This volume contains a selection from the papers that were originally presented at the Fifth Workshop on Uncertainty in Artificial Intelligence, held on August 18th to 20th, 1989 at the University of Windsor in Ontario, Canada. The papers have been edited in the light of workshop discussions and, in some cases, expanded. It also includes written versions of commentaries by discussants on selected workshop sessions. This fifth volume from the fifth annual workshop may be seen as marking a milestone for the field. The first Workshop on Uncertainty in Artificial Intelligence in 1985 brought together for the first time what was then something of a fringe group as far as mainstream artificial intelligence (AI) research was concerned. The early meetings focussed particularly on the fundamental issues of representing uncertainty, and they were the scene of vigorous and sometimes heated debates about the relative merits of the competing approaches. In more recent meetings these "religious wars" have much diminished in intensity. This is not because participants have all finally reached a concensus. Far from it! But perhaps rather because there is a recognition that arguments purely about the fundamentals, important though they may be, are not sufficient to settle the question of selecting one scheme over another for a particular application. More pragmatic criteria must also be considered. What are the computational demands of a scheme? How reliable is it? How easy is it to structure and encode human uncertain knowledge into the formalism? Can the model and reasoning be explained comprehensibly to users? The primary goal is not simply to convince rival uncertainty theorists of the superiority of your approach over theirs. Indeed, if we believe Thomas Kuhn's characterization of the clash between scientific paradigms is applicable here, this ambition may often be unattainable anyway. Rather the goal is to provide AI practitioners and knowledge engineers with tools for uncertain reasoning that are not only principled but also practical for handling their real world problems. In this view the criterion of success of an approach is its effectiveness for application. The marketplace for ideas, like more tangible goods, is ultimately ruled more by consumers than producers. This more pragmatic emphasis is much in evidence in this volume. While it does contain some interesting papers on fundamentals (Chapter I), and particularly on the relationships between uncertain and defeasible reasoning (Chapter II), the bulk of the papers address more practical issues. Recognizing that tractable probabilistic inference is critical for large scale applications of Bayesian belief nets, the papers in Chapter III explore the development of more efficient algorithms. The papers in Chapter IV discuss the embedding of uncertain inference schemes in general software tools for building knowledge-based systems. Chapter V contains papers exploring a range of important issues in knowledge acquisition, modelling and explanation. Chapter VI includes a wide variety of different approaches to uncertain inference applied to problems in vision and recognition, including natural language understanding. The final Chapter, VII, VI presents comparisons of uncertain inference schemes, both theoretical and empirical. Ward Edwards, in his provocative commentary on these papers "System Condemnation Pays Off", argues for the key role of empirical comparisons in the development of the field. The final panel discussion in the third workshop started by addressing the question "Why does mainstream AI research ignore uncertainty research?", which provoked the answer "Why does uncertainty research ignore mainstream AI research?" On the evidence from this volume and the increasing prevalence of papers addressing uncertain reasoning in general AI forums such as the AAAI and IJCAI conferences and AI Journal, this jibe is no longer apt. The application of recently developed uncertain reasoning schemes to important problems in AI, including medical diagnosis, machine diagnosis, vision, robotics, and natural language understanding among others, demonstrate the increasing effectiveness of uncertainty researchers in addressing the needs of mainstream AI. As AI tools are applied to larger scale problems the importance of the explicit treatment of uncertainty is becoming increasingly inescapable. Uncertainty research is also making important contributions to a range of fundamental theoretical issues including nonmonotonic and default reasoning, heuristic search, planning, and learning. While some participants in earlier workshops may feel a lingering nostalgia for the cut and thrust excitement that characterized the youth of the field a scant few years ago, we should welcome these developments as signs of the increasing maturity and broader impact of the field. Max Henrion, Palo Alto, California. xi Reviewers Gautam Biswas Jack Breese Piero Bonissone Peter Cheeseman Paul Cohen Marvin Cohen Gregory Cooper Bruce D'Ambrosio Norm Dalkey Rina Dechter Michael Fehling Ken Fertig Matthew Ginsberg Dan Geiger David Heckerman Eric Horvitz Henry Kyburg John Lemmer Tod Levitt Ron Loui Enrique Ruspini Ramesh Patil Judea Pearl Prakash Shenoy David Spiegelhalter Michael Wellman Ben Wise Ron Yager The editors give heartfelt thanks to the reviewers, who contributed generously of their time in refereeing the papers for the Workshop and so helped provide the basis for this volume. Program Committee Piero Bonissone Tod Levitt Peter Cheeseman Ramesh Patil Paul Cohen Judea Pearl Laveen Kanal Enrique Ruspini Henry Kyburg Glenn Shafer John Lemmer Lotfi Zadeh Program Chair: General Chair: Max Henrion Ross Shachter xi Reviewers Gautam Biswas Jack Breese Piero Bonissone Peter Cheeseman Paul Cohen Marvin Cohen Gregory Cooper Bruce D'Ambrosio Norm Dalkey Rina Dechter Michael Fehling Ken Fertig Matthew Ginsberg Dan Geiger David Heckerman Eric Horvitz Henry Kyburg John Lemmer Tod Levitt Ron Loui Enrique Ruspini Ramesh Patil Judea Pearl Prakash Shenoy David Spiegelhalter Michael Wellman Ben Wise Ron Yager The editors give heartfelt thanks to the reviewers, who contributed generously of their time in refereeing the papers for the Workshop and so helped provide the basis for this volume. Program Committee Piero Bonissone Tod Levitt Peter Cheeseman Ramesh Patil Paul Cohen Judea Pearl Laveen Kanal Enrique Ruspini Henry Kyburg Glenn Shafer John Lemmer Lotfi Zadeh Program Chair: General Chair: Max Henrion Ross Shachter Xlll Contributors Alice Agogino University of California, Berkeley, CA 94720 John Mark Agosta Stanford University, Stanford, CA 94305 K.M. Andress Purdue University, W. Lafayette, IN 47907 Fahiem Bacchus University of Waterloo, Waterloo, Ontario N2I-3G1 Kenneth Basye Brown University, Providence, Rl 02912 Michael P. Beddoes University ofB. C, Vancouver, B.C. V6T 1W5 R. Bellazzi Universita di Pavia, Pavia, Italy Carlo Berzuini Universita di Pavia, Pavia, Italy Thomas O. Binford Stanford University, Stanford, CA 94305 Piero P. Bonissone General Electric Corporation, Schenectady, NY 12301 Lashon Booker Naval Research Laboratories, Washington D.C, 20375-5000 John S. Boose Boeing Computer Services, Seattle, WA 98124 Jeffrey Bradshaw Boeing Computer Services, Seattle, WA 98124 John S. Breese Rockwell International, Palo Alto, CA 94301 Kate Bull Hospital for Sick Children, London, England M.S. Carroll Purdue University, W. Lafayette, IN 47907 Kuo-Chu Chang Advanced Decision Systems, Mountain View, CA 94040 Eugene Chamiak Brown University, Providence, Rl, 02912 R. Martin Chavez Stanford University, Stanford, CA 94305 Paul B. Chou IBM T.J. Watson Research Center, Yorktown Heights, NY 10598 Marvin S. Cohen Decision Science Consortium, Inc., Reston, VA 22091 Gregory F. Cooper Stanford University, Stanford, CA 94305 Stanley Covington Boeing Computer Services, Seattle, WA 98124 David A. Cyrluk General Electric Corporation, Schenectady, NY 12301 Thomas Dean Brown Univeristy, Providence, Rl 02912 Ward Edwards University of Southern California, Los Angeles, CA 90089. Christopher Elsaesser Carnegie Mellon University, Pittsburgh, PA 15213. Kenneth W. Fertig Rockwell International, Palo Alto, CA 94301 R.C.G. Franklin Hospital for Sick Children, London, England Robert Fung Advanced Decision Systems, Mountain View, CA 94040 Dan Geiger University of California, Los Angeles, CA 90024 Robert Goldman Brown University, Providence, Rl 02912 Moises Goldszmidt University California - Los Angeles, CA 90024 James W. Goodwin Knowledge Analysis, Belmont, MA Benjamin Grosof IBM T.J. Watson Research Center, Yorktown Heights, NY 10598 XIV David Heckerman Stanford University, Stanford, CA 94305 Max Henrion, Rockwell International, Palo Alto, CA 94301 and Carnegie Mellon University, Pittsburgh, PA 15213 Peter D. Holden McDonnell Douglas Corporation, St. Louis, MO 63166 J.D. Horton University of Saskatchewan, Canada S7N OWO and University of New Brunswick, Fredericton, Canada E3B 5A3 Naveen Hota JAYCOR, Vienna, VA 22180 Avi C. Kak Purdue University, W. Lafayette, IN 47907 Harold P. Lehmann Stanford University, Stanford, CA 94305 Paul E. Lehner George Mason University, Fairfax, VA 22030 Tod S. Levitt Advanced Decision Systems, Mountain View, CA 94040 J.R. Lewis Purdue University, W. Lafayette, IN 47907 C. Lopez-Abadia Purdue University, W. Lafayette, IN 47907 Ronald P. Loui Washington University St. Louis, MO 63130 William R. Moninger National Oceanic and Atmospheric Administration, Boulder, CO 80303 Theresa M. Mullin Decision Science Consortium, Inc., Reston, VA 22091 Eric Neufeld University of Saskatchewan, Canada S7N OWO and University of New Brunswick, Fredericton, Canada E3B 5A3 Judea Pearl University of California, Los Angeles, CA 90024 Mark Peot Stanford University, Stanford, CA 94305 and Rockwell International, Palo Alto, CA 94301 Bruce Perrin McDonnell Douglas Corporation, St. Louis, MO 63166 David Poole University of British Columbia, Vancouver, B.C. V6T 1W5 Gregory M. Provan University of British Columbia, Vancouver, B.C. V6T 1W5 Connie L. Ramsey Naval Research Laboratories, Washington D.C, 20375-5000 Stuart Russell University of California, Berkeley, CA 94720 Peter Russo Boeing Computer Services, Seattle, WA 98124 Ross D. Shachter Stanford University, Stanford, CA 94305 Philippe Smets I.R.I.D.I.A., Université Libre de Bruxelles, Brussels, Belgium B-1050 David J. Spiegelhalter MRC Biostatlstlcs Unit, Cambridge, England Sampath Srinivas Rockwell International, Palo Alto, CA 94301 Jonathan Stillman General Electric Corporation, Schenectady, NY 12301 Michael J. Swain University of Rochester, Rochester, NY 14627 David S. Vaughan McDonnell Douglas Corporation, St. Louis, MO 63166 Thomas Verma University of California, Los Angeles, CA 90024 L.E. Wixson University of Rochester, Rochester, NY 14627 Yang Xiang University of British Columbia, Vancouver, B.C. V6T 1W5 Robert Yadrick McDonnell Douglas Corporation, St. Louis, MO 63166 Uncertainty in Artificial Intelligence 5 M. Henrion, R.D. Shachter, L.N. Kanal, and J.F. Lemmer (Editors) © Elsevier Science Publishers B.V. (North-Holland), 1990 3 Lp—A Logic for Statistical Information Fahiem Bacchus* Department of Computer Science University of Waterloo Waterloo, Ontario, Canada N2L-3G1 [email protected] 1 Introduction This extended abstract presents a logic, called Lp, that is capable of representing and reasoning with a wide variety of both qualitative and quantitative statistical information. The advantage of this logical formalism is that it offers a declarative representation of statistical knowledge; knowledge represented in this manner can be used for a variety of reasoning tasks. The logic differs from previous work in probability logics in that it uses a probability distribution over the domain of discourse, whereas most previous work (e.g., Nilsson [2], Scott et al. [3], Gaifman [4], Fagin et al. [5]) has investigated the attachment of probabilities to the sentences of the logic (also, see Halpern [6] and Bacchus [7] for further discussion of the differences). The logic Lp possesses some further important features. First, Lp is a superset of first order logic, hence it can represent ordinary logical assertions. This means that Lp provides a mechanism for integrating statistical information and reasoning about uncertainty into systems based solely on logic. Second, Lp possesses transparent semantics, based on sets and probabilities of those sets. Hence, knowledge represented in Lp can be understood in terms of the simple primative concepts of sets and probabilities. And finally, the there is a sound proof theory that has wide coverage (the proof theory is complete for certain classes of models). The proof theory captures a sufficient range of valid inferences to subsume most previous probabilistic uncertainty reasoning systems. For example, the linear constraints like those generated by Nilsson's probabilistic entailment [2] can be generated by the proof theory, and the Bayesian inference underlying belief nets [8] can be performed. In addition, the proof theory integrates quantitative and qualitative reasoning as well as statistical and logical reasoning. * Support for preparing this paper was provided through a grant from the University of Waterloo, and NSERC grant OGP0041848. Parts of this work have been previously reported at CSCSI-88 [1].
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