Table Of ContentLecture Notes ni Artificial Intelligence
Subseries of erutceL setoN ni Computer ecneicS
Edited by .J Siekmann
Lecture Notes ni Computer Science
Edited by G. Goos dna .J sinamtraH
Editorial
Artificial Intelligence has become a major discipline under the roof of
Computer Science. This is also reflected by a growing number of titles
devoted to thisf ast developing field to be published in our Lecture
Notes in Computer Science. To make these volumes immediately vis-
ible we have decided to distinguish themb y a special cover as Lecture
Notes in Artificial Intelligence, constituting a subseries of the Lecture
Notes in Computer Science. This subseries is edited by an Editorial
Board of experts from all areas of AI, chaired by grOJ Siekmann, who
are looking forward to considerf urther A! monographs and proceed-
ings of high scientific quality for publication.
We hope that the constitution of this subseries wilble well accepted
byt he audience of the Lecture Notes in Computer Science, and we
feel confident that the subseries will be recognized as an outstanding
opportunity for publication by authors and editors of thAeI community.
Editors and publisher
Lecture Notes ni
Artificial Intelligence
yb detidE .J nnamkeiS
Subseries of Lecture Notes ni Computer Science
485
.K Furukawa H. Tanaka
.T Fujisaki (Eds.)
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,oykoT ,napaJ July 12-14, 9891
Springer-Verlag
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Editorial Board
D. Barstow W. Brauer R Brinch Hansen D. Gries D. Luckham
C. Moler A. Pnueli G. Seegm(Jller J. Stoer N. Wirth
Volume Editors
Koichi Furukawa
ICOT Research Center
Mita Kokusai Building ,=112 4-28 Mita 1-chome
Minato-ku, Tokyo 108, Japan
Hozumi Tanaka
Department of Computer Science
Tokyo Institute of Technology, 12-10h-okayama 2-chome
Meguro-ku, Tokyo 152, Japan
Tetsunosuke Fujisaki
IBM, Thomas J,Watson Research Center
P.O. Box 704, Yorktown Heights, NewYork 10598, USA
CR Subject Classification (1987): ,1.1.D D.1.3, D.3.1-3, 1=.4.1-2, ,1.2.1 1.2.3-5,
?.2.1
ISBN 3-540-53919-0 Springer-Vertag Berlin Heidelberg NewYork
ISBN 0-387-53919-0 Springer-Verlag New York Berlin Heidelberg
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Foreword
This volume of the Springer Lecture Notes in Artificial Intelligence contains
selected papers presented at the Eighth Logic Programming Conference (July -21
,41 Tokyo). 1989, It is the successor to Volumes 221,264, 315 and 383.
The contents cover various topics of logic programming: reasoning,
programming language, concurrent programming, knowledge programming,
natural language processing ando ther applications.
The first section contains one invited talk by Prof. Donald Michie, the chief
scientist of the Turing Institute. The title of his presentation is "Human and
Machine Learning of Descriptive Concepts". The paper is based on the oral
presentation of his talk and is transcribed from ICOT Journal, No. 27, 1990. It
introduces .various . research . . results . on . learning conducted by Prof. Michie's group
at the Turmg Instltute includingd ata classification, extraction of classification
rules of propositional level and inductive inference of predicate logic formulas.
The second section, "Reasoning" contains three papers. The first paper, by
J.Yamaguchi, discusses the issue of concept classification, particularly exceptional
cases, and relates the problems to non-monotonic reasoning. The second paper, by
F. Dong and H. Nakagawa, introduces a new class of program called conditional
programs and discusses their declarative semantics using the perfect model
semantics proposed by Przymsinski. The third paper, by .Y Shi and .S Arikawa,
tries to formulate time-bounded reasoning and discusses such issues as the
soundness, completeness, truth maintenance and reliablility of the inference
procedure.
The third section, "Logic Programming Language", contains one paper by
A. Yamamoto, which gives a theoretical foundation of EFS (Elementary Formal
System) as a logic programming language. It can be regarded as a constraint logic
programming language on strings, and therefore could be useful in natural
language processing.
The fourth section is on "Concurrent Programming", and contains two
papers. One is by J. Tatemura and H. Tanaka, presenting a debugger for a parallel
logic programming language, Fleng, introduced by .M Nilsson. The second paper,
by H. Numazaki et al., proposes a parallel generalized LR parser, an extension of
Tomita's algorithm, written in a concurrent logic language, GHC.
The fifth section is on "Knowledge Programming", and contains one paper
by Y. Hosono et al. The paper describes a system called "Knowledge Media
Station" which provides users with a flexible and powerful tool for incorporating
and organizing knowledge represented by a well-formed language with various
types of knowledge using a hypermedia framework. Hosono also tries to show that
logic programming and hypermedia meet in several respects.
The sixth section is about "Natural Language Processing" and contains two
papers. The first paper, by H. Tsuda et al., presents a kind of constraint logic
programming language, called cu-Prolog, for combinatorial problems. The paper
also describes cu-Protog's application to JPSG (Japanese Phrase Structure
Grammar) parser. The way cu-Prolog solves the constraints is based on program
transformation, which is unique and very powerful. The second paper of this
section, by T. Tamura et al., proposes an efficient table-driven bottom-up parser
written in Prolog.
Iv
The last section is about "Applications". Two papers are presented. The
first one is by T. Shintani and proposes a consistency maintenence mechanism in
subjective judgements based on an AHP (Analytic Hierarchy Process) pairwise
comparison method. The consistency maintenence is done by the TMS (Truth
Maintenance System) embedded in a Prolog-based production system. The second
paper, by H. Nakamura et al., describes a logic design assistance system built on
top of a temporal logic based language called Tokio.
We hope that reader will find useful information in these proceedings.
Finally we thank program committee members, reviewers and authors for their
cooperation and patience. We especially would like to thank the reviewers from
the IBM Watson Research Center for their thorough efforts to improve the quality
of the English as well as the contents of all submitted papers.
Tokyo, Winter 1990 Koichi Furukawa
Hozumi Tanaka
Tetsunosuke Fujisaki
Table of Contents
Invited Talk
Donald Michie
Human and Machine Learning of Descriptive Concepts
Reasoning
J. Yamaguchi
The Classification and Boundary Problem 23
F. Dong, H. Nakagawa
Semantics of Non-monotonic Reasoning Based on Perfect Model 38
Y. Shi, S, Arikawa
Time-Bounded Reasoning in First Order Knowledge Base Systems 54
Logic Programming Language
A. Yamamoto
Elementary Formal System as a Logic Programming Language 73
Concurrent Programming
J. Tatemura, H. Tanaka
Debugger for a Parallel Logic Programming Language Fleng 87
H. Numazaki, N. Tamura, and H. Tanaka
Parallel Generalized LR Parser Based on Logic Programming 97
iiiv
Knowledge Programming
.Y H. Hosono, Kumagai, H. Shimizu, .M Sumida, .A Takeuchi,
N. Takiguchi, .T Wake and .T Yamaoka
Knowledge Media Station 701
Natural Language Processing
.H Tsuda, K. Hasida and H. Shirai
cu-Prolog and its Application a to JPSG Parser 431
N. Tamura, H. Numazaki and .H Tanaka
Table-Driven Bottom Up Parser in Prolog 441
Application
.T Shintani
Consistency A Maintenance Mechanism Subjective for Judgements
and Its Application 361
.H Nakamura, .M Nakai, .S Kono, .M Fujita and .H Tanaka
Logic Design Assistance Using Temporal Based Logic Language
Tokio 471
Executive Committee I Program Committee 1 Editorial Board
Hozumi Tanaka (Conference Chairman) Tokyo Institute of Teclmology
Koichi Furukawa (Program Chairman) ICOT
Tetsunosuke Fujisaki IBM
Eiiti Wada The University of Tokyo
Kazuhiro Fuchi ICOT
Ryuzo Hasegawa ICOT
Kazunori Ueda ICOT
Tatzuya Hagino Kyoto University
Susumu Kunifuji IIAS-SYS Fujitsu Ltd.
Toshiaki Kurokawa IBM Japan
Yuji Matsumoto Kyoto University
Fumio Mizoguchi Science University of Tokyo
Shin-ichi Morishita IBM
Hiroshi Nakagawa Yokohama National University
Katsumi Nitta ETL
Akihito Sakurai Ilitachi, Ltd.
Akikazu Takeuchi Mitsubishi Electric Corporation
Hisashi Taniguchi ICOT
Yoshito Toyama Nrr
Ilideki Yasukawa Matsushita Electric Industrial
Co.,Ltd.
Referees
A. Aiba J. Akahani K. Appleby-Hougham J. Arima
A. Arimura T. Chikayama T. Fujisaki Y.I' uj 'i wara
K. Furukawa T. Hagino T. Hama K. Handa
T. Narada M. Haraguchi R. Hasegawa K. Hashida
R. Helm S. Hirose T. Hirose S. Honiden
K. Horiuchi K. Inoue T. Ishida M. Ishizuka
H. Isozaki T. It0 K. Iwanuma H. Iwasalri
Y. Kameyama T. Kanamori Y. Kimura H. Kitakami
A. Konagaya S. Kunifuji J-L. Lassez M. Maeda
H. Matsubara H. Matsuda K. Matsumoto Y. Matsumoto
H. Maruyama T. Minami K. Miura T. Miyachi
T. Miyazaki I?. Mizoguchi H. Motoda H. Mori
S. Morishita K. Mukai I-I. Nakagawa J. Nakamura
A. Nigam S. Nishio K. Nitta Y. Noda
M. Numao H. Ohwada I. Ogata M. Ogawa
Y. Ravin K. Sakai C. Sakama A. Sakurai
K. Sato T. Sato M. Schor H. Seki
P. Sheridan E. Shibayarna T. Shintani H. Shirai
H. Sugano T. Sugawara R. Sugimura H. Suzuki
M. Tajima A. Takeuchi H. Taki H. Tamaki
N. Tamura H. Tanaka J. Tanaka S. Tojo
T. Tokunaga Y. Toyama K. Ueda T. Ukita
W. Wilsson J. Yamaguchi A. Yamamoto H. Yasukawa
M. Yasumura M. Yokoo K. Yokota N. Yonezaki
A. Yonezawa K. Yoshida T. Yuasa
HUMAN AND MACHINE LE~ING OF DESCRIPTIVE CONCEPTS
Donald Michie
The Turing Institute
George House, 36 North Hanover Street, Glasgow G1 2AD, U,K.
I am speaking today of machine learning, because I believe that its
study is the logical and necessary next step for the ten years which lie
ahead of us. My reason is that we have a common goal inspired by the
idea of the automatic creation and management of large bodies of
knowledge; N not mere data or mere information, but organized,
structured knowledge of human type. The current status of machine
learning is illustrated in Fig. .A1
The knowledge enterprise requires not only powerful methods of
automatic deduction, which are capable of deriving facts from theories,
but also the history of human knowledge accumulation, which we call
"science", proves to us that it is also necessary to have powerful
mechanisms for constructing theories from facts. The second of these
processes is known as "inductive inference", the logic oriented branch of
machine learning.
The first process of deductive reasoning by machine and
incorporation into programming language has seen much progress,
particularly in the last ten years. But when we move to learning, to
inductive inference, we find the situation rather undeveloped.
In the area of commercial application, rather simple methods of
rule induction at the propositional level of Boolean formulas and decision
trees, machine learning has been very successful, and continues to
generate substantial financial payback for those companies in various parts
of the world for whom specialist induction engineering companies
develop applications.
