rick@cs.arizona.edu (Rick Schlichting) (12/29/90)
[Dr. David Kahaner is a numerical analyst visiting Japan for two-years
under the auspices of the Office of Naval Research-Asia (ONR/Asia).
The following is the professional opinion of David Kahaner and in no
way has the blessing of the US Government or any agency of it. All
information is dated and of limited life time. This disclaimer should
be noted on ANY attribution.]
[Copies of previous reports written by Kahaner can be obtained from
host cs.arizona.edu using anonymous FTP.]
To: Distribution
From: David K. Kahaner, ONR Asia, [kahaner@xroads.cc.u-tokyo.ac.jp]
B. Chandrasekaran, Ohio State U., [bxc@sumex-aim.stanford.edu]
Hiroshi Motoda, Hitachi, Ltd. [motoda@harl.hitachi.co.jp]
Re: First Japanese Knowledge Acquisition for Knowledge-Based
Systems Workshop (JKAW-90), 25-31 October, 1990, Kyoto & Saitama Japan.
27 Dec 1990
ABSTRACT. The first Japanese Knowledge Acquisition for Knowledge-Based
Systems Workshop (JKAW-90), was held October 25-31, 1990, one (open)
part in Kyoto, and an invited part at Hitachi's Advanced Research
Lab. Professor B. Chandrasekaran and Dr. Hiroshi Motoda prepared the
following summaries.
INTRODUCTION.
The Japanese Knowledge Acquisition Workshop was held as a 2-part
workshop, an "open" session in Kyoto where more than 300 attended
gathered to listen to mostly invited talks on knowledge acquisition, and
a "closed" session held at the Hitachi Advanced Research Laboratory in
Saitama, where one invited talk, a panel and a number of submitted
papers were presented.
Professor B. Chandrasekaran,
Laboratory for AI Research
The Ohio State University
Columbus, OH 43210 USA
who is currently on Leave at
Knowledge Systems Laboratory
Stanford University
Palo Alto, CA
[bxc@sumex-aim.stanford.edu]
has provided the following summary and evaluation of the open Workshop.
Dr. Hiroshi Motoda
Advanced Research Laboratory
Hitachi, Ltd.
Hatoyama, Saitama 350-03, Japan
[motoda@harl.hitachi.co.jp]
provided a description of the closed Workshop as well as the titles and
author's affiliations for presented papers.
OVERVIEW.
As one might expect, a subject such as knowledge acquisition (KA) can
cover a broad territory, and the talks reflected this breadth of the
subject matter. Nevertheless, a few categories could be identified.
Talks by Clancey, Chandrasekaran, Gruber and McDermott in the open
session and numerous talks in the closed session focused on the
relationship between knowledge, tasks, and the domains. Knowledge
acquisition as modeling the domain for tasks of different types was a
theme that was made explicitly by Clancey, and implicitly by others in
this group. The talk by Quinlan in the open session and several talks in
the closed session dealt with the problem of learning classification
rules from examples. Using Explanation-Based Learning for acquiring
knowledge was also discussed. A number of talks discussed what one
might call KA support structures, such as hypertext and natural language
text processing to produce intermediate structures which could then be
accessed by the knowledge engineer for further formalization. Interview
techniques, and computer support for interviewing, also attracted
attention. In what follows, Chandrasekaran provides a summary of the
proceedings of the open part of the workshop. Following that is a short
summary of the closed workshop, from H. Modota. A list of the papers
presented and authors names and addresses are appended.
CONCLUSION. (Chandrasekaran)
Part of the research in knowledge acquisition closely follows research
in the more general issues of knowledge systems. Thus a major part of
the material covered in this workshop reflected some of the major
advances in knowledge systems research of the last several years, such
as task-specific architectures, classification problem solving in
particular and compilation from deep models. There was also a
substantial interest in the knowledge acquisition front-end itself: how
to simplify the process of getting the knowledge into the machine.
Interviewing, analysis of natural language text, and a variety of
learning techniques are the major dimensions of research in this
direction. Finally, it is very clear that the Japanese have made a
major commitment to knowledge systems, and the breadth of applications,
the degree of commitment and progress reported by Japanese industry,
government, academia in the knowledge systems area were very impressive
to this visitor.
DETAILED SUMMARY (OPEN SESSION).
The Kyoto part of the workshop started with a couple of tutorials by
Brian Gaines of the University of Calgary and John Boose of Boeing AI
Center, both of whom have been very active in putting together a series
of international knowledge acquisition workshops. Gaines has developed a
comprehensive model of the social and organizational aspects of knowledge
acquisition. He used this framework to motivate the issues, as he saw
them, in knowledge acquisition. In his view, the knowledge acquisition
bottleneck is still very much with us: we still lack the variety of tools
needed to help knowledge engineers transfer knowledge to shells, shells
are still quite limited in the kinds of tasks they can help us with, and
we still have a variety of problems in validating, maitaining and
upgrading knowledge bases. Nevertheless there has been considerable
progress as well. The first generation of KA tools were based on human
interviewing. The second generation saw the emergence of a number of
computer-based tools that helped in the acquisition process. In his
view, the so-called third generation tools will provide KA environments
supporting a wide range of complementry tools and techniques.
John Boose's tutorial surveyed the development of KA technology by using
the concept of mediating representations. By mediating representations
he means problem modeling languages that help bridge the gap between
experts and computer implementations. In a sense most of the research in
expert systems and KA in the last decade can be said to be in this area.
He surveyed manual and computer based methods and tools. In the manual
category are a variety of ideas such as brainstorming, interviewing,
protocol analysis techniques. Semi-automated methods include shells or
KA interfaces that use the structure of the generic problem (e.g,
Heracles for heuristic classification, CSRL for hierarchical
classification, Acquinas for general analysis problems, and so on) to
help structure and represent knowledge. In addition to the tools that
support KA for various types of problems are various semi-automated tools
that support knowledge acquisition more generically: tools to support
interviewing, textual reprsentation (e.g., hypercards), modeling tools,
tools to support acquisition of knowledge from multiple experts, and so
on. Finally there is the body of research and tools that help in fully
automated KA: tools based on machine induction for concept learning
(important for classification problems).
The invited talks started with B. Chandrasekaran's presentation on
knowlege acquisition for real-time (RT) problems. His work has focused
on the close relation between the structure of the task and the knowledge
needed for it. He has been part of the movement in knowlege-based
systems that has emphasized the task-structure as the mediating agency in
knowledge acquisition and knowledge representation. He applied that
point of view to the design of knowledge systems for RT problems. He
started by noting that all problem solving works by using models of the
world, and to the extent that these models cannot be guaranteed complete
or correct, no problem solving process can guarantee that solutions
generated by it will be correct. In RT problems, the goal of problem
solving is to generate action sequences that will keep the environment in
desired states in the face of changes and disturbances. Chandrasekaran
noted that since success in this enterprise cannot be guaranteed, the
best that can be done is to know how to abandon goals that are not being
achieved and replace them with goals that are more likely to be
achieved. Thus all realistic RT problems have a built-in goal structure
which helps in making goal abandonment and substitution decisions
rapidly. Chandrasekaran also noted that power in successful RT problem
solving in humans comes about, not from complex reasoning at run-time
about resources or by various forms of "meta-reasoning", but by avoiding
complex run-time reasoning altogether. Instead, power arises from four
specific sources: good design of sensor and action systems so that direct
mappings from observations to internal states of importance, and from
internal states to needed ations can be made as directly as possible; a
pre-compiled body of knowledge that helps to order goals in terms of
priorities and preferences, so that goals can be abandoned and modified
as needed; and another body of knowledge that helps to synthesize action
sequences, i.e., plans. Chandrasekaran proposed that this form of task
analysis is very useful for knowledge acquisition since it identifies
what types of knowledge to seek in the domain.
William Clancey proposed a very general framework for qualitative
modeling of domains and tasks. Expert systems can be viewed as programs
that use and construct models of some system in the world so that it can
be assembled, repaired, controlled, etc. In his view, the major difference
between conventional computer programs and expert systems is that the
models in latter systems represent processes and structures by relational
networks. Control knowledge for constructing such a model can be
described as operators that construct a graph linking processes and
structures causally, temporally, spatially, by subtype, etc. Clancey did
not think that expert systems are models of intelligence as much as they
provide a new and powerful way to model systems and processes. He also
proposed that "recurrent macrostructures" in such models-- both of
objects and processes -- can be shared. He related a wide body of work -
- Heracles-DX, Blackboard-ACCORD, generic tasks, and role-limiting
methods in a unified framework.
Hiroshi Motoda presented a joint research program whose aim is to build
interviewing systems for KA. Their system supports KA in the domain of
logical design of databases. The task description is used by the authors
to give the interview system substantial capabilities for knowledge
analysis. In particular, they can identify which knowledge is lacking or
erroneous by actually solving a problem, and refine the knowledge base as
it goes through a problem solving process. It helps in making ambiguous
knowledge explicit by forcing the user to think about the way a problem
is being solved, and is able to handle requirements that were not thought
of in the beginning. The authors then proposed a general architecture
for KA based on their experience with the above KA system. The proposed
architecture is intended to incorporate machine learning techniques,
especially by failure analysis.
Quinlan is known as the originator of ID3, a very successful inductive
learning system for learning classification rules from examples. In his
talk he reviewed the progress of such approaches, and the wide range of
applications in which they have been used. One of the deficiencies of
ID3 and related algorithms is that they are not applicable for structured
data, i.e., data with complex relationship to other data. Instead of a
data set where each object has a fixed number of attributes, in the more
complex example, each object may have an open-ended number of attributes
and also may be related to other objects by means of an open-ended number
of relations. He proposed the use of first-order logic to describe the
relations. He described some experiments on learning such relations.
Shigenobu Kobayashi presented a jointly developed survey of the work on
KA in Japan. Not surprisingly, Japanese research in this area covered a
wide territory. It was clear to me that knowledge systems are a major
focus of research in Japan. They reported on a recent study by Japanese
Information Processing Center of a number of expert system projects in
Japan, and the lessons for KA that resulted from this study. Task
analysis and identification of the problem solving functions involved in
the task are a hard first step. Domain models can be acquired in the
form of generic prototypical libraries without waiting for the system
development stage. No uniform formal methods emerged as the most
appropriate. This report proposed a general architecture for KA with
components to support different requirements of the different stages in
the Life Cycle.
It appears that a number of systems are being developed to support the
interview process. Quite a bit of interest seems to exist in guiding
knowledge acquisition by task-oriented methods, i.e., by methods that
take advantage of the relation between knowledge and the structure of the
task. Examples of these methods are the Generic Task methods of this
author, and the Heuristic Classification work of Clancey.
Another idea that has been discussed in the US for a decade or more is
knowledge compilation. (My work with Sanjay Mittal was one of the
earliest in this area, which I followed up with work on compiling
diagnostic knowledge from deep models. The DART project of Genesereth is
another fore-runner.) It appears that in Japan quite a few projects are
investigating this approach to KA. Qualitative simulation and
explanation-based learning are being investigated for compiling high-
level rules from structural models of devices.
They reported on a substantial body of research on KA from text. A
system called hmU performs automatic model construction from hardware
manuals for CAD systems. Research on acquiring concepts from natural
language texts was also reported.
There is extensive research on machine learning techniques. Inductive
and similarity learning techniques were reported. A variety of research
on Explanation-Based Learning was discussed. A new technique called
Frustration-Based Learning by Motoda was discussed. FBL uses frustrated
states during problem solving processes as motive forces for learning.
It associates a frustration element with information relevant to it.
Case-based reasoning as a way of acquiring knowledge also seems to be an
active area of research.
A major initiative is worth mentioning. Advanced Software Technology and
Mechatronics Research Institute of Kyoto is setting up a project on a
large-scale engineering knowledge base under collaboration with academic
and industrial research organizations.
John McDermott reported on the research by him and his colleagues at DEC
on making applications programming easier. They are building three
tools: Spark, Burn and Firefighter. To quote from McDermott: "Spark
interviews a developer and on the basis of what the developer says about
the information available for and the results desired from a task, Spark
will select, from a library of pre-defined computational mechanisms, one
or more mechanisms that can collectively produce the results desired on
the basis of the information available. Spark will then configure those
mechanisms into a problem solving method.
"Burn will retrieve, from a library of KA tools, the tool associated with
each of the mechanisms selected by Spark. It will use those tools to
elicit expertise from the developers and produce an application program
by encoding that expertise in a way that will allow it to be used by the
problem-solving method previously configured by Spark.
"Whenever the application program is used by one of the developers,
Firefighter will ask whether the program has performed correctly and if
it has not, will invoke either Burn or Spark to add knowledge to the
program or modify the problem solving method.."
According to McDermott, Spark takes its inspiration from the generic task
work of Chandrasekaran and from Bennet's Roget program. He expects that
the insights that will emerge from Spark are:
Based on the characteristics of a task (at the non-programmer level),
appropriate pre-defined computational mechanisms can be identified and
configured to perform the task, and the number of such mechanisms need
not be large to cover a large number of interesting applications.
Burn is based on the idea that KA can be guided by the problem solving
methods and mechanisms. Firefighter is a relatively simple
"diagnostician" of the problem solving method or knowledge, and uses a
variety of techniques to "fix" the knowledge or the method.
Tom Gruber described two knowledge acquisition problems that require the
domain expert to formalize and operationalize knowledge that is currently
expressed only informally. One is the problem of acquiring strategy: how
to elicit strategic knowledge from experts without requiring them to
design procedures. The other is the problem of device teleology: how to
elicit explanations of the "purpose" of a device (i.e. what functions
are served by aspects of its design). A technique called justification-
based knowledge acquisition, developed for the first problem, was used by
the knowledge acquisition tool ASK. Gruber discussed how ASK acquired
strategies for diagnostic test selection in a medical domain by asking
experts to justify specific choices in a diagnostic context. The
justification language is a user-extensible set of features that abstract
states of the diagnosis in a running knowledge system. Then he described
a generalization of the technique that could be used to elicit design
rationale for electromechanical devices. In the proposed system, the
user justifies a design decision by demonstrating the behaviors enabled
or prevented by the designed structure using a qualitative simulation,
comparing alternative designs in the same simulation context. Again,
features that justify one design decision over another are states of a
running computer model of the process--in this case, a simulation instead
of a diagnosis.
The human uses his access to the external world and his richer knowledge
base by selecting what is relevant among the possibilities afforded by
the computational model, and providing new vocabulary terms in terms of
old terms. Since the knowledge is stated in the language of the machine,
and the machine is made to formulate it in the context of its task, the
additional knowledge is always operational.
The final talk of the open conference was by Alain Rapaport of Neuron
Data who outlined an architecture that his company is developing to
integrate a large number of different types of problem solving and KA
modules.
BRIEF SUMMARY (CLOSED SESSION).
Approximately forty people attended the closed meeting. Papers
presented appear in the proceedings listing below. Discussions centered
around two main topics - "What is knowledge?" and " Knowledge Reuse."
Many metaphors were used to describe knowledge and knowledge
acquisition. All of them generated controversy. Examples include the
"chicken soup," "filtering process," and "bottleneck" metaphors for
knowledge acquisition. A comment was that it might be better to avoid
distracting metaphor discussions and concentrate on interesting work.
The theme of knowledge reuse raised during the open meeting dominated
many of the presentations and discussion. Many systems are attempting
to reuse knowledge at different levels to gain leverage during knowledge
acquisition (task knowledge, problem- solving knowledge, task
ontologies). A comment was that it might not be realistic to expect the
work to get very far very quickly since most success in knowledge-based
systems seems to come from domain-specific knowledge, not from
high-level reusable domain templates or procedures.
----------------------------------------------------------------
JKAW-90 Proceedings
Proceedings are available as Proceedings of the First Japanese
Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW '
90), Motoda, H., Mizoguchi, R., Boose, J., and Gaines, B. (Eds.),
Ohmsha Ltd., 3-1 Kanda Nishiki-cho, Chiyoda-ku, Tokyo 101, Japan.
Papers in the proceedings include:
Invited Papers
Gaines, B., Shaw, M. (1990). Foundations of knowledge acquisition,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 3-24.
Boose, J. (1990). Knowledge acquisition tools, methods, and
mediating representations, Proceedings of the First Japanese
Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW '
90), Ohmsha Ltd.: Tokyo, pp. 25-64.
Clancey, W. (1990). Implications of the system-model-operator
metaphor for knowledge acquisition, Proceedings of the First
Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop
(JKAW '90), Ohmsha Ltd.: Tokyo, pp. 65-80.
Kawaguchi, A., Motoda, H., Mizoguchi, R. (1990). An architecture
of knowledge acquisition by interview based on dynamic analysis,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 81-96.
Quinlan, J. R. (1990). Inductive knowledge acquisition from
structured data, Proceedings of the First Japanese Knowledge
Acquisition for Knowledge-Based Systems Workshop (JKAW '90), Ohmsha
Ltd.: Tokyo, pp. 97-112.
Kobayashi, S., Terano, T., Motoda, H., Mizoguchi, R. (1990).
Research activities of knowledge acquisition and learning in Japan,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 113-133.
McDermott, J., Dallemagne, D., Klinker, G., Marques, D., Tung,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 134-147.
Gruber, T. (1990). Justification-Based Knowledge Acquisition,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 148-158.
Rule Induction
Leung, K., Wong, M. (1990). AKARS-1: An automatic knowledge
acquisition and refinement system, Proceedings of the First
Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop
(JKAW '90), Ohmsha Ltd.: Tokyo, pp. 161-174.
Tsujino, K., Takegaki, M., Nishida, S. (1990). A knowledge
acquisition system that aims at integrating inductive learning and
explanation-based learning, Proceedings of the First Japanese
Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW '
90), Ohmsha Ltd.: Tokyo, pp. 175-190.
Medow, M., Travis, L. (1990). Novice: Getting textbook knowledge
into expert systems, Proceedings of the First Japanese Knowledge
Acquisition for Knowledge-Based Systems Workshop (JKAW '90), Ohmsha
Ltd.: Tokyo, pp. 191-206.
Hagiwara, K. (1990). A new approach to classification knowledge
refinement, Proceedings of the First Japanese Knowledge Acquisition
for Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 207-218.
Webb, G. (1990). Rule optimization and theory optimization:
Heuristic search strategies for data driven machine learning,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 219-232.
Hypertext
Cunningham, P., Fujisawa, H., Hederman, L., Cummins, F. (1990). A
combined approach to text retrieval using concept networks linked
to hypertext, Proceedings of the First Japanese Knowledge
Acquisition for Knowledge-Based Systems Workshop (JKAW '90), Ohmsha
Ltd.: Tokyo, pp. 235-248.
Langendorfer, H., Schreiweis, U., Hofmann, M. (1990). Knowledge
acquisition with a special hypertext system, Proceedings of the
First Japanese Knowledge Acquisition for Knowledge-Based Systems
Workshop (JKAW '90), Ohmsha Ltd.: Tokyo, pp. 249-258.
EBL/CBR
Nakamura, K., Kobayashi, s. (1990). Knowledge acquisition from
machine adjustment cases using causal model and operationality
criteria, Proceedings of the First Japanese Knowledge Acquisition
for Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 261-276.
Mizoguchi, R., Matsuda, K., Nomura, Y. (1990). ISAK: Interview
system for acquiring design knowledge - A new architecture of
interview systems using examples, Proceedings of the First Japanese
Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW '
90), Ohmsha Ltd.: Tokyo, pp. 277-286.
Interview
Hori, K., Ohsuga, S. (1990). Assisting the articulation of the
mental world, Proceedings of the First Japanese Knowledge
Acquisition for Knowledge-Based Systems Workshop (JKAW '90), Ohmsha
Ltd.: Tokyo, pp. 289-300.
Bergadano, F., Giordana, A., Saitta, L. (1990). Automated versus
manual knowledge acquisition: A comparison in a real domain,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 301-314.
Taki, H., Tersaki, S. (1990). A proposal guided knowledge
acquisition support system, Proceedings of the First Japanese
Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW '
90), Ohmsha Ltd.: Tokyo, pp. 315-330.
Tijerino, Y., Kitahashi, T., Mizoguchi, R. (1990). A task analysis
interview system that uses a problem-solving model, Proceedings of
the First Japanese Knowledge Acquisition for Knowledge-Based
Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo, pp. 331-344.
Yoshida, K., Motoda, H. (1990). Hierarchical knowledge
representation based on approximations, Proceedings of the First
Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop
(JKAW '90), Ohmsha Ltd.: Tokyo, pp. 345-360.
Miscellaneous
Barfoursh, A., Rogers, M. (1990). A knowledge acquisition method
for distributed information systems: A multi-database approach,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 363-381.
Spirgi, S., Probst, A., Wenger, D. (1990). Knowledge acquisition
in a development methodology for knowledge-based applications,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 382-397.
Tajima, M. (1990). A framework of learning system RLS, Proceedings
of the First Japanese Knowledge Acquisition for Knowledge-Based
Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo, pp. 398-409.
Papers not presented at the workshop
Gaines, B. (1990). Knowledge representation servers: A generic
technology for knowledge acquisition and knowledge-based systems,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 413-430.
Mo, D. (1990). Acquiring text editing programs from examples,
Proceedings of the First Japanese Knowledge Acquisition for
Knowledge-Based Systems Workshop (JKAW '90), Ohmsha Ltd.: Tokyo,
pp. 431-447.
Names and addresses of authors in JKAW-90.
--------------------
Ahmad A. Barfoursh
University of Bristol
Department of Computer Science
Queen's Building, Room 0.65
University Walk, Bristol BS8 1TR
England
ahmad@compsci.bristol.ac.uk
John Boose
Advance Technology Center
Boeing Computer Services,
Bldg. 33.07
2760 160th Ave. SE
Bellevue, Washington,
USA 98008
john@atc.boeing.com
B. Chandrasekaran
Computer and Information Science Department
Ohio State University
Columbus, OH 43210-1277
USA
chandra@cis.ohio-state.edu
bxc@sumex-aim.stanford.edu
William J. Clancey
Institute for Research on Learning
2550 Hanover St., Palo Alto,
CA 94304
USA
clancey.pa@xerox.com
Padraig Cunningham
Hitachi Dublin Laboratory
O'Reilly Institute
Trinity College
Dublin 2
Ireland
cnnnghmp@vax1.tcd.ie
Hiromichi Fujisawa
Central research Laboratory
Hitachi, Ltd.
1-280 Higashi-Koigakubo,
Kokubunji, Tokyo 185
Japan
fujisawa@crl.hitachi.co.jp
Brian Gaines
Department of Computer Science
University of Calgary
2500 University Dr. NW
Calgary, Alberta,
Canada T2N 1N4
gaines@cpsc.ucalgary.ca
Attilio Giordana
Dipartimento di Informatica
Universita' di Torino
C.so Svizzera 185, 10149
Torino
Italy
saitta@itoinfo.bitnet
Thomas Gruber
Knowledge Systems Lab
Deaprtment of Computer Science
Stanford Unioversity
701 Welch Road, Building C
Palo Alto, CA 94304
USA
gruber@sumex-aim.stanford.edu
Kenichi Hagiwara
The 2nd System Development Section
The 1st System Development Department
Systems Engineering Group
Fujifacom Corporation
1, Fuji-machi, Hino-shi,
Tokyo 191
Japan
Koichi Hori
Research Center for Advanced
Science and Technology
The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153
Japan
hori@ohsuga.u-tokyo.ac.jp
Atsuo Kawaguchi
Advanced Research Laboratory
Hitachi, Ltd.
Hatoyama, Saitama 350-03
Japan
atsuo@harl.hitachi.co.jp
Shigenobu Kobayashi
Department of Systems Science
Graduate School of Science and
Engineering at Nagatsuda
Tokyo Institute of Technology
4259 Nagatsuda, Midori-ku,
Yokohama 227
Japan
kobayasi@sys.titech.ac.jp
K. S. Leung
Computer Science Department
The Chinese University of Hong Kong
Shatin, N.T.
Hong Kong
ksleung%cucsd.cuhk.hk@uunet.uu.net
John McDermott
DEC, Dlb 5-3/E2,
290 Donald Lynch Blvd.,
Marlboro, MA 01752
USA
mcdermott@airg.enet.dec.com,
jmd@cs.cmu.edu
Mitchell Medow
Department of Computer Science
University of Wisconsin-Madison
1210 W. Dayton Street
Madison, WI 53706
U.S.A
Mailing Adress
604K Eagle Heights
Madison, WI 53705
U.S.A
medow@cs.wisc.edu
Riichiro Mizoguchi
Institute of Scientific and
Industrial Research
Osaka University
8-1 Mihogaoka, Ibaraki, Osaka 567
Japan
miz@ei.sanken.osaka-u.ac.jp
Dan H. Mo
ACTC Technologies Inc.
350, 6715-8 Street, N.E.
Calgary, Alberta,
Canada T2E 7H7
Hiroshi Motoda
Advanced Research Laboratory
Hitachi, Ltd.
Hatoyama, Saitama 350-03
Japan
motoda@harl.hitachi.co.jp
Kotaro Nakamura
Research Team of Information Science
Engineering Research Laboratory
Japan Tobacco Inc.
1-31 Kurobegaoka, Hiratsuka,
Kanagawa 254
Japan
John Ross Quinlan
Department of Computer Science
University of Sydney, Basser
Sydney, NSW 2006
Australia
quinlan@basser.cs.su.oz.au
Alain Rappaport
Neuron data
444 High Street
Palo Alto, California 94301
USA
atr@ml.ri.cmu.edu
Lorenza Saitta
Dipartimento di Informatica
Universita' di Torino
C.so Svizzera 185, 10149
Torino
Italy
saitta@itoinfo.bitnet
Uwe Schreiweis
Technische Universit"{at
Braunschweig Institut f"{ur
Betriebssysteme und
Rechnerverbund B "{ultenweg
74/75 D-3300 Braunschweig
Fed. Rep. Germany
schrei@tubsibr.uucp
Susan Spirgi
IBM Basel
Hirschg"{asslein 11
CH-4010 Basel
Switzerland
Morihiko Tajima
Inference Laboratory
Intelligent Information Department
Electrotechnical Laboratory
1-1-4 Umezono, Tsukuba-City, 305
Japan
tazima@etl.go.jp
Hirokazu Taki
Mitsubishi Electric Corp.
Information Systems and Electronics
Dev. Lab.
ESG
5-1-1 Ofuna, Kamakura, Kanagawa
Japan
taki@isl.melco.co.jp
Takao Terano
Graduate School of Systems Management
The University of Tsukuba, Tokyo
3-29-1 Otsuka, Bunkyo-ku, Tokyo 112
Japan
terano@gssm.otsuka.tsukuba.ac.jp
Yuri A. Tijerino
Institute of Scientific and
Industrial Research,
Osaka University
8-1 Mihogaoka, Ibaraki, Osaka 567
Japan
yuri@ei.sanken.osaka-u.ac.jp
Katsuhiko Tsujino
System 4G,
Central Research Laboratoary,
Mitsubishi elec. corp.,
8-1-1, Tsukaguchi-Honmachi,
Amagasaki, Hyugo 661
Japan
tsujino@sys.crl.melco.co.jp
Geoffrey I. Webb
Department of Computing and
Mathematics
Deakin University Geelong,
Victoria 3217
Australia
webb@cm.deakin.oz.au
Dieter Wenger
Swiss Bank Coporation
Hochstrasse, 16
CH-4053 Basel
Switzerland
Kenichi Yoshida
Advanced Research Laboratory
Hitachi, Ltd.
Hatoyama, Saitama 350-03
Japan
yoshida@harl.hitachi.co.jp
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