Advances in Artificial Intelligence: Natural Language and Knowledge-based Systems

314 Pages·1990·18.077 MB·

by  Ze’ev Ben-Porat, Martin Charles Golumbic (auth.), Martin Charles Golumbic (eds.)

Advances in Artificial Intelligence
Martin Charles Golumbic
Editor
Advances in
Artificial Intelligence
Natural Language and
Knowledge-based Systems
With 64 Figures
Springer-Verlag
New York Berlin Heidelberg
London Paris Tokyo Hong Kong
Martin Charles Golumbic
IBM T.1. Watson Research Center
Yorktown Heights, NY 10598
IBM Israel Scientific Center
Technion City
Haifa, Israel
Printed on acid-free paper.
© 1990 by Springer-Verlag New York Inc.
Softcover reprint of the hardcover 1st edition 1990
All rights reserved. This work may not be translated or copied in whole or in part without the written
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Camera-ready text prepared by the authors.
9 8 7 6 5 432 I
ISBN-13 :978-1-4613-9054-1 e-ISBN-13 :978-1-4613-9052-7
001: 10.1007/978-1-4613-9052-7
If a man declares to you that he has found facts and confirmed
them by experience - even though this man is considered most
reliable and highly authoritative, be cautious ... Weigh his
opinions and theories critically according to the requirements of
pure reason.
Moses Maimonides (1135-1204)
Contents
Introduction ix
Acknowledgements xii
Contributors xiii
Learning from Experience in Board Games 1
Ze'ev Ben-Porat and Martin Charles Golumbic
PRODS: A Prototype Based Design Shell for Prototype
Selection and Prototype Retinement 26
Rivka E. Oxman
What's in a Joke? 43
Michal Ephratt
Machinery for Hebrew Word Formation 75
Uzzi Oman
Theory Formation for Interpreting an Unknown Language 95
Ephraim Nissan
Ontology, Sublanguage, and Semantic Networks
in Natural Language Processing 114
Victor Raskin
An Incremental Conceptual Clustering Algorithm
that Reduces Input-Ordering Bias 129
Yo elle S. Maarek
Anticipating a Listener's Response in Text Planning 145
Ingrid Zukerman
Towards an Intelligent Finite Element Training System 171
Alex Bykat
Bayesian Inference in an Expert System without Assuming
Independence 182
Alex Gammerman and A.R. Thatcher
viii
A Partial Orders Semantics for Constraint Based Systems 219
Steven A. Battle
Partial Orders as a Basis for KBS Semantics 227
Simon P. H. Morgan and John G. Gammack
A Heuristic Search Approach to Planning and Scheduling
Software Manufacturing Projects 247
Ali Safavi and Stephen F. Smith
From Data to Knowledge Bases 269
Martin Charles Golumbic and Dennis Grinberg
Index 295
Introduction
Research in artificial intelligence, natural language processing and knowledge-based
systems has blossomed during the past decade. At national and international symposia
as well as in research centers and universities all over the world, these subjects have
been the focus of intense debate and study. This is equally true in Israel which has
hosted several international forums on these topics. The articles in this book represent
a selection of contributions presented at recent AI conferences held in Israel.
A theoretical model for a system that learns from its own experience in playing board
games is presented in Learning from Experience in Board Games by Ze'ev Ben-Porat
and Martin Golumbic. The model enables such a system to enhance and improve its
playing capabilities through the use of a learning mechanism which extracts knowledge
from actual playing experience. The learning process requires no external guidance or
assistance. This model was implemented and tested on a variant of "Chinese
Checkers." The paper shows the feasibility and validity of the proposed model and
investigates the parameters that affect its performance traits. The experimental results
give evidence of the validity of the model as a powerful learning mechanism. Original
and general algorithms for knowledge extraction and pattern matching were designed
and tested as part of the prototype computer system. Analysis of the performance
characteristics of these algorithms indicates that they can handle large knowledge bases
in an efficient manner. After an initial amount of knowledge has been acquired,
further learning can take place with practically no effect on the response time of the
system. Further research is necessary in order to realize the fulI potential of this model
and its applicability to different domains.
In PRODS: A Prototype Based Design Shell for Prototype Selection and Prototype
Refinement, Rivka Oxman presents a system for encoding and employing multiple
prototypes in design. In knowledge-based systems, prototypes provide a basis for the
generation of localized designs that are made specific through modification within a
refmement process. They also enable the generalization of situations and constraints
into prototypical contexts and problem contexts. The PRODS system provides a
representation of two complementary types of knowledge which operate in a
refmement process. Generative knowledge describes the design solution space by
predefined refinement stages; interpretive knowledge enables selection and control. It
is suggested that such systems can be made to interface with external CAD systems
and to incorporate other kinds of design knowledge.
In the paper What's in a loke? by Michal Ephratt, a preference algorithm is formulated
for identifying and grasping the unexpected meaning, i.e., the punch line, of a linguistic
joke. The usual task in computational linguistics is identifying and resolving ambiguity
in favor of the most probable or likely meaning. By contrast here, a modification
called "partial reverse preference" is applied to a variety of preference parsers, such as
x
Schubert's trade-off preference algorithm, turning them into electronic comedians.
Watch out Bob Hope! This is demonstrated in the paper with several side-splitters.
Machinery for Hebrew Word Formation by Uzzi Oman provides a new formal and
arguably computer-implementable approach to morphological inflexion and derivation,
while applying it to Hebrew. Since Hebrew text is generally written without vowels,
written words display a higher degree of ambiguity presenting more of a challenge to
computational linguistics than many other languages. Moreover, Hebrew grammar
reflects concepts which allow interpretation of texts from ancient through medieval into
modem times. The author's erudition as one of Israel's foremost Hebrew linguists
leads us to view in a new light the processes of obtaining new roots, generating
compound words, adopting foreign words, forming of "new" words by children, plus
additional aspects of the morphological machinery.
In contrast to Hebrew, which has been in continuous use from Biblical times to the
present, the Etruscan language of the pre-Roman civilization of northern Italy has
been totally dead and buried for almost two millennia. In his paper Theory Formation
for Interpreting an Unknown Language, Ephraim Nissan relates a methodology of
research interpreting the surviving corpus of this ancient language. It is suggested that
the problem of deciphering Etruscan is but one of many ways in which AI
methodology could be applied to the deciphering of unknown languages.
Victor Raskin argues for a formal foundation of meaning representation in natural
language artificial intelligence in the paper Ontology. Sublanguage. and Semantic
Networks in Natural Language Processing. State of the art techniques neither in
linguistic semantics nor in model-theoretic semantics provide solutions to the major
problems of natural language processing semantics. An alternative is proposed that
exploits the sublanguage orientation nature of NLPS and the ability to predefine the
grain size of ~he required meaning analysis by combining the ontological and semantic
network bases approaches.
Conceptual clustering has been introduced in machine learning research both as an
extension to numerical clustering and as a method of learning by observation. The
goal of conceptual clustering is not only to identify a cluster as a group of similar
objects, as in classical numerical clustering, but also to determine its implicit
conceptual structure. Incremental clustering techniques are especially desirable for
applications in which context constantly evolves, but they are very sensitive to the
ordering of the initial input. In An Incremental Conceptual Clustering Algorithm that
Reduces Input-Ordering Bias, Yoelle Maarek provides a hierarchical clustering method
which allows for overlapping of clusters. This overlap requires ordering biases by
periodically upgrading the whole hierarchy. The algorithm is presented both from the
viewpoint of machine learning and of cluster analysis theory. A formal analysis of its
computational complexity is given.
In the process of generating text, writers generally take into consideration the effect
their words are likely to have on their listeners. In particular, they try to prevent
possible comprehension problems which are likely to be triggered by the text. In her
paper Anticipating a Listener's Response in Text Planning, Ingrid Zukerman presents a
xi
mechanism which emulates this behavior in the generation of discourse to convey an
intended message. This mechanism anticipates the effect of a given message on a
model of listener's beliefs, and proposes rhetorical devices to preclude possible adverse
effects.
To understand and effectively use a sophisticated statistical, numerical, or other large
software package requires many hours of training and guided practice with an expert.
In Towards an Intelligent Finite Element Training System, Alex Bykat describes the
construction of a knowledge-based consulting and training system for a fInite element
package. After presenting the overall design, the paper concentrates on the system's
natural language communication.
Bayesian Inference in an Expert System without Assuming Independence by Alex
Gammerman and A.R. Thatcher describes an application of Bayesian inference to the
problem of estimating from past data, the probabilities that patients with certain
symptoms have certain diseases. The study relates to 2000 patients at a hospital in
Scotland who suffered acute abdominal pain. The methodology applies Bayes'
Theorem without assuming independence of the symptoms and yet without an
unmanageable increase in complexity. Moreover, using a limited database, it is shown
how to select combinations of symptoms which allow the calculation of confidence
bounds for the probabilities most relevant to the diagnosis of each disease.
Two papers address the problem of formulating practical semantics for constraint
based systems: A Partial Orders Semantics for Constraint Based Systems by Steven
Battle and Partial Orders as a Basis for KBS Semantics by Simon Morgan and John
Gammack. Central to this problem is the issue of partial solutions which give rise to
partially ordered structures of representational states. The partial order and the
operations that may be performed upon it provide a general way of talking about
constraint based systems without the need for specillcs of any particular
representational scheme.
In their paper A Heuristic Search Approach to Planning and Scheduling Software
Manufacturing Projects, Ali Safavi and Stephen Smith discuss an incremental approach
to scheduling which allows trade-offs between productions with different resource
capacities and requirements. Since software project planning is seen more as a
schedule revision problem than a schedule generation problem, an incremental strategy
is advised. A formal treatment of revision operators is presented for application during
the scheduling process, and the findings of an implementation of their model are
reported.
Research into knowledge bases is an area still under evolution. Slowly, however, basic
principles are emerging which seem to have the resilience to stand up to scientific rigor.
In From Data to Knowledge Bases, Martin Golumbic and Dennis Grinberg reflect on
and analyze the progress made so far and identify problems which can reasonably be
attacked in the near term. Included as an appendix, is the edited transcript of a panel
discussion on the subject, held at the Third International Conference on Data and
Knowledge Bases (Jerusalem, June 1988).
Acknowledgements
First and foremost, I would like to thank. the authors whose papers reflect so well the
advances being made in artificial intelligence, natural language and knowledge-based
systems. The first four papers were prepared for this volume as an outgrowth of talks
presented in 1987 and 1988 at the Israeli National Conference on Artificial Intelligence
held annually at the end of December. The remaining papers are based on the
applications track of BISFAI-89, the Bar-Ilan Symposium on the Foundations of
Artificial Intelligence (June 1989), sponsored by the Research Institute for the
Mathematical Sciences at Bar-Ban University with additional support from IBM Israel.
(Several theoretical papers from that symposium will appear in a special issue of the
Annals of Mathematics and Artificial Intelligence.)
I would like to take this opportunity to thank Ariel Frank, my co-chairman of
BISFAI-89, for his suburb effort in handling all of the organizational, financial and
logistic arrangements. Without his help and participation the symposium could never
have been the success that it was. We express our appreciation to our colleague Uri
Schild who was in charge of the social arrangements. Special thanks go to the invited
hour speakers: Joseph Halpern (IBM Research) "Reasoning about Knowledge and
Probability," Johann A. Makowsky (Technion) "The Architecture of Concepts," John
McCarthy (Stanford University) "Formalized Common Sense Knowledge and
Reasoning," Judea Pearl (U.C.L.A.) "Graphoids and the Representation of
Dependencies," and Ronald Rivest (M.LT.) "Recent Developments in Machine
Learning Theory." Their enlightening lectures gave focus to the entire symposium.
I am especially grateful to my wife, Lynn Pollak Golumbic, who assisted with the
editing during breaks from her environmental activities and who has constantly helped
to provide an environment conducive to my professional activities. I would like to
express my appreciation to Lynn Montz, formerly of Springer-Verlag, who was
instrumental in initiating the appearance of this volume. Most of the effort spent
editing this book was while I was a visitor at the IBM Thomas J. Watson Research
Center, Yorktown Heights, New York, and I thank them for their support. Finally, I
am indebted to the referees whose comments and suggestions improved many of the
expositions.
MARTIN CHARLES GOLUMBIC