Table Of ContentMachine 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].
