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 ---------------END OF REPORT----------------------------------------