Received: from ics.uci.edu by paris.ics.uci.edu id aa29760; 27 Jun 94 13:15 PDT Received: from ics.uci.edu by q2.ics.uci.edu id aa04913; 27 Jun 94 13:14 PDT To: ML-LIST: ; Subject: Machine Learning List: Vol. 6 No. 17 Reply-to: ml@ics.uci.edu Date: Mon, 27 Jun 1994 12:50:03 -0700 From: Michael Pazzani Message-ID: <9406271314.aa04913@q2.ics.uci.edu> Machine Learning List: Vol. 6 No. 17 Monday, June 27, 1994 Contents: MLnetNEWS 2.3 The Machine Learning List is moderated. Contributions should be relevant to the scientific study of machine learning. Mail contributions to ml@ics.uci.edu. Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues may be FTP'd from ics.uci.edu in pub/ml-list/V/ or N.Z where X and N are the volume and number of the issue; ID: anonymous PASSWORD: ---------------------------------------------------------------------- Date: Fri, 24 Jun 94 14:16:19 0000 From: MLnet Admin Subject: MLnetNEWS 2.3 MLnet NEWS 2.3 May 1994 Text-only version ********************************** (If you want to be added to the mailing list of the "printed" newsletter please contact us at : mlnet@csd.abdn.ac.uk Tel: +44 224 272304 Fax: +44 224 273422 *********************************** CONTENTS: - The 2nd Familiarization Workshop Series at Catania - Decisions and News from the Catania MB/TC Meetings - Policy Statement from the Network of excellence in Machine Learning - ECML-94 Community Meeting - ECML-95 First Announcement and Call for Papers - MLnet Sponsored Familiarization Workshops, Heraclion, Crete - MLnet Workshop on Industrial Applications of Machine Learning - MLnet summer School on Machine Learning and Knowledge Acquisition - Technical Reports on the Catania Familiarization Workshops - Theory Revision and Restructuring - Knowledge Level Models of Machine Learning - Declarative Bias - Machine Learning and Statistics - Focus on Machine Learning research at Daimler-Benz - Funding for Workshops/Meetings - Guidelines for Proposals to MLnet - Putting Together a Machine Discoverer: Basic Building Blocks (J.Zytkow) - Mobal 3.0 released - News from the University of Dortmund - 1996 International Machine Learning Conference - Call for Proposals - Technical Meetings between MLnet and other Networks - News from our "data-base" of Industrial applications - Procedures for Joining MLnet - List of Main and Associate Nodes - Documents available from Aberdeen *********************************** The 2nd Familiarization Workshop Series at Catania FAMILIARIZATION WORKSHOPS COMMUNITY MEETING Each person attending the Workshops had been provided with an Annual Report, and a Policy Document; both of which were presented in MLnet's new folder! (For the convenience of other readers who have not seen the Policy Document it is reproduced on page 4 of this newsletter). Derek Sleeman introduced the session, which was organised as 4 parts: % A brief review of the Annual Report with comments and discussion; % A discussion of the Policy Document; % A summary of the relevant parts of the Fourth Framework Program; % Highlights of the Catania Management Board and Technical Board Meetings. The first three items are discussed here; the fourth is reported independently on page 3. Annual Report The Convener Derek Sleeman, summarised the organisational structure of MLnet with its five Technical Committees: Electronic Communication, Industrial Liaison, Research, Training, and Written Communication, and reported that the Management Board and Technical Committees had met 3 times in 1992/93 at Leuven, Vienna and Blanes. MLnet had been involved indirectly in the organisation of the 1993 European Conference on Machine Learning (ECML-93), and the associated Workshops held in Vienna in April 1993. MLnet organized a Familiarization workshop in Blanes (ES) in September 1993; there were four subworkshops: "Learning and Problem Solving" (coordinator: Maarten van Someren, Amsterdam); "Multi- strategy Learning" (coordinator: Lorenza Saitta, Torino); "Machine Discovery" (coordinator: Peter Edwards, Aberdeen) and "Learning in Autonomous Agents" (coordinator: Walter van de Velde, Brussels). Additionally at Blanes we held a Community meeting to discuss MLnet's activities and its plans; and further we held a final overview session where each of the workshop coordinators presented the key issues which had arisen in their sessions. This led to a very important exchange of ideas, where technical details, issues of research methodology, and industrial relevance were intermingled. For more details, see MLnet News 2,1. Newsletter MLnet published three Newsletters, in 1993. Regular features include: - reports on MLnet events - details of future events - conference/meeting reviews - profile of one of the network's groups - list of forthcoming events worldwide in ML/KA - outline of recent (PhD) theses We hope to include a feature shortly on industrially available ML/KA tools. The Newsletter is now sent to 467 persons, many of whom receive additional copies for local/national distribution. The last two issues have also been distributed electronically in Europe and internationally via the MLlist at UC Irvine. Copies of our Newsletters have been available at several of the major AI/ML events in 1993 including ECML93 (Vienna), ML93(Amherst, Mass), IJCAI93 (Chambery, France) and AAAI-93 (Washington) Collection of information by Technical Comittees The Industrial Liaison, Research and Technical Committees all sent out questionnaires to collect information relevant to their goals. Thus, the Industrial Liaison Technical Committee sought to determine the ML & KA tools which are commercially available, and to identify Industrial projects current in European industry. The Research Committee sought to acquire information about ongoing Research projects in European HEIs and industrial Research Laboratories. Similarly, the Training Technical Committee sought to identify courses taught in ML and KA in European HEIs. (These surveys were carried out essentially by the Conveners of the corresponding Technical Committees with some support from the other members of the committees and the network.) FTP and Amsterdam facilities A Machine Learning FTP Archive has been installed at GMD (Bonn) which provides the users with electronic access to ML related papers, technical reports, data and software. The archive is continuously updated and extended by the MLnet partners. An email server is now installed at the University of Amsterdam which provides the members of MLnet with an automated emailing list for distributing materials and announcements to the subscribers. For more details please see MLnet Newsletter 2,1. Contacts with other networks and broader dissemination ESPRIT week 1992 featured a session on Basic Research where each of the Networks was asked via a telelink to highlight its plans and activities; at the end of the formal session delegates visited the NoE booths for more information. Booths were, in fact, manned for the whole of ESPRIT week. There was an Inter-Networks meeting in May 1993, which focused on the provision of computer networking infrastructure for the NoEs and specifically on a plan proposed by CABERNET that all the NoEs should use the Andrew File system. MLnet agreed to actively investigate the facilities which its nodes require, and the facilities provided by the Andrew system. There was a further meeting of Networks held in Brussels in late September 1993 where the major topics for discussion were: - the provision of a computer-network infrastructure for all Networks (a continuation of the discussion held in May), - the roles of NoEs both within their communities and as potential policy-influencers within the EC. - funding in Framework-4 Bob Wielinga represented MLnet at both these meetings; Bob has produced a report on the last meeting which was presented to the Management Board in December, 1993, and published in MLnews 2,2. Later the same day, the several representatives of NoEs met the Energy subcommittee of the European Parliament to informally discuss Networks and their activities and plans. Again Bob Wielinga represented MLnet at this meeting. The Academic Coordinator, Derek Sleeman, has had considerable informal contact with several of the Networks' Coordinators through the year by email, phone and several face-to-face meetings. Membership Applications in 1992/93 We received a considerable number (32) of initial enquiries about MLnet. Many of these are from groups outside Europe or from very small groups, and so the direct result is that their names have been added to the mailing list for the Newsletter. However nine led to formal applications to join MLnet as Associate nodes. Of the applications received, three groups have been successful, namely the groups at Kaiserslautern, Karlsruhe and Catania. At this point the Convener was rightly asked to explain why the spend in year 1 had been such a small percentage of the available monies. His explanation included the following points: % Projects often have a slow start thus spending more money in later years. % In the case of the NoE, the contract was considerably different from standard ESPRIT contracts and so we were all determining the "rules". Essentially, as noted elsewhere, NoEs are able to support infra-structure and not Research or Training per se. % Monies had been allocated to items in year 1 (such as a Summer School and a Network Machine) but it had not been possible to actually carry out these activities. % The percentage of the budget allocated for the second year was considerably higher. It was further suggested that Networking infrastructure could be a big item if the NoE expands, as it intends, into Central and Eastern Europe. Policy Document Derek Sleeman (DS) explained the Research Technical Committee was producing a revised State-of-the-Art report (the original was part of the Proposal/Technical Annex). It was expected this would be available within 2-3 months; Lorenza Saitta, as Convener of the Research Technical Committee, was responsible for its production. Because decisions were being made about the detail for the Fourth Framework in early 1994, it was suggested to the Academic Coordinator that it would be timely if MLnet produced a short Policy Document (see page 4) which essentially included a set of themes which this community felt should be present in the next program, together with any comments we might have on how future programs might be better organised and administered. These then were the principal elements of the Policy Document produced by DS, with input from the authors of the several sections of the State-of-the-Art Document. Several drafts were circulated to members of the Management Board (including David Cornwell, the Project Officer) for comment. The resulting document has now been circulated to all nodes, presented formally to the EC, and, together with the Annual Report, formed a major focus for discussion which DS had with the Commission in March of this year (more in the next section). The policy document was then discussed extensively, when the following points were raised: % Data Mining should have been included as one of the "themes". % Some felt more emphasis should have been given to Applications. % Some felt that the themes should be structured into techniques and applications. This then lead naturally into a review of comments received on the Policy Document from the EC's officials and an overview of the Fourth Framework Program. A Summary of the Fourth Framework Program and a visit to Brussels The total allocated budget for the 4th Framework Program is expected to be around 12 billion ecu of which 2 billion ecu will be for Long Term Research and R&D in IT. The 2 billion ecu are to fund all aspects of IT research, i.e. not only the IT industry but also industries which are IT users. Approximately 10% of this budget is for Long Term Research (and this figure probably excludes Networks). Additionally, it is believed that 2% of the total "IT" budget is to be set aside for training and mobility which are to be administrated by the "Basic Research" office. The Commission is aiming for a call in September, but this is thought to be optimistic by many involved. However, it is generally expected that new contracts will start during '95. DS reported that the annual report and the policy document has been the focus of a seminar he had given in Brussels to Project Officers, that he had had detailed discussions with policy makers in the Directorate, and with a number of individual project officers from both the BRA and Industry divisions. Subsequently he had had a detailed phone call with Simon Bensasson. The EC's officials had discussed the current plans for the next program. The policy document included a number of themes which the Management Board felt should be included in the next program. DS reported that he had been reassured that officials believed that to achieve many of the aspects inherent in the next R&D program, it would be necessary to use Machine Learning, Knowledge Acquisition and second generation ES technology. (The phrase Adaptive System had been used). The program is, in fact, to be organized around 4 focussed clusters: % Open Microprocessor Systems initiatives; % High Performance Computing & Networking; % Integration in manufacturing; % Technologies for Business Processes; and so the techniques of Adaptive Systems are seen as being orthogonal enabling technologies. Further, DS was told that the call for Long Term Research (formerly Basic Research) would include an open call, and hence any theme could be proposed. Additionally, several of the points raised about the administration of projects were accepted in principle; these included: % generally having only small and well focussed consortia; % that Long Term Research projects would consist largely of 3-year projects as well as some 1-year projects to try out ideas. (Earlier we had heard that LT would be composed exclusively of 1-year projects.) % regular call for proposals would be helpful to both industry and academia; % more formal links, particularly at the level of LT Research actions, should be enhanced with Japan and America. Additionally, DS reported on a meeting with the Director of the Human Capital & Mobility Programme, M. de Nettancourt, (this topic was reported at the Management Board but not at the general meeting - it is included here for completeness)where they discussed: % the overall structure and aims of NoEs like MLnet; % the fact that HC&M covers all of Science and is oriented towards the Basic Research end of the spectrum; % the future plans for the HC&M directorate in the next Framework: % the earlier plan was to drop the institutional fellowships programme as these were seen as being unwieldy to administer. (We now understand that this may be modified as several NoEs have made representations to their national representatives.) % there is a proposal to introduce the concept of European Laboratory Without Walls, i.e. several groups working on joint research (LT Research) topics. (We agreed that there was a considerable complementarity between the ESPRIT Basic Research Networks and the HC&M programme; the former providing the infrastructure to support Research & Training and the HC&M program being able to support actual researchers and research teams.) Derek Sleeman closed the meeting by asking that people send him, or any member of the Management Board, any other comments which might subsequently occur to them. Also he stressed again MLnet's openness to suggestions for events and activities which might be organised. Luc deRaedt thanked Derek Sleeman for his many efforts on behalf of the Network. *********************************** Decisions and News from the Catania MB/TC Meetings % Electronic Communication: It was agreed that an advanced FTP service (at GMD) and an experiment in the use of the Andrew File System (based at Amsterdam) should both be supported. % Reports or Databases will be produced shortly by the Industrial Liaison, Research and Training Technical Committees. % A revised State of the Art Report will be available shortly. % Joint ELSNET*/MLnet Workshop to be supported (dates to be announced). % IFIP-94; A Workshop on ML applications at IFIP is to be supported by MLnet. % New nodes: Daimler-Benz (main node); EDF (Electricit de France), Prague and Oxford (associate nodes). % ECML-95 and Familiarisation Workshops to be supported by MLnet (see further information on pages 5-7). % Strategic Planning: To be featured as a specific item at each of the next 3 Management Board meetings. % The EC has agreed to support MLnet for the third year (ie until September 1995). For support after this date MLnet will need to apply to the Fourth Framework. *********************************** POLICY STATEMENT FROM THE NETWORK OF EXCELLENCE IN MACHINE LEARNING IMPORTANCE OF THE AREAS OF MACHINE LEARNING (ML) AND KNOWLEDGE ACQUISITION (KA). Building Knowledge Bases has been identified as a major bottleneck in producing Intelligent Systems. ML and KA have developed a number of tools and techniques which can help considerably with this critical phase. Additionally, techniques have been devised to refine knowledge bases when the results produced by an intelligent system differ from those anticipated by the domain expert. We expect in the next decade that learning components will be embedded in many of the IT systems produced, so that they can progressively improve their Knowledge Bases. Further, we expect to see major integration of AI techniques, including ML, with traditional DP techniques. Additionally, ML is having a considerable impact on reducing costs of developing software for controlling dynamic systems, such as robots. MLnet believes that our subfield is central to building tomorrow's Intelligent Systems, and thus is central to every forward-looking European IT company. MLnet's CURRENT AIM The aim of the network is to coordinate Machine Learning Research and Development throughout Europe, to ensure that these technologies become a pervasive force in European industry, and at the same time to consolidate and enhance their solid scientific bases. STRATEGICALLY IMPORTANT ISSUES MLnet's Research Organization and Coordination Technical Committee has recently identified the following topics of paramount importance, to achieve the goals given above: % The design, implementation and extensive use on demanding tasks of Workbenches which integrate Machine Learning and Knowledge Acquisition tools, together with Problem Solving systems (complex tasks are likely to require a Multi-strategy learning approach). % Ability of a KBS (Knowledge Based System) to refine its KB (Knowledge Base) as a result of feedback received from domain experts, or from the environment; together with the ability to tailor its explanations to the level/sophistication of its user. % Refinement of computer programs, given feedback from experts, and the environment. % Enhancing Inductive Logic Programming, which studies how to induce first order logic formulae from observations and background knowledge, to deal with complex induction problems in scientific discovery, knowledge acquisition, automatic programming and deductive databases. % Reusable Knowledge Bases. % The ability of a Robot to interact with its world and learn to interpret sensor signals and actions. % Genetic Algorithms allow vast sets of hypotheses which occur in many domains, say such as molecular biology and genetics, to be searched (as Genetic Algorithms allow parallelism to be exploited). COMMENTS ON MANAGEMENT OF PROJECTS AND PROGRAMS ESPRIT, by any standards, has been a successful vehicle for Basic Research and Research and Development in Europe. Against great odds it has managed to get Research groups across two major divides, namely transnational and academic/industrial, working together. Now that this tradition of cooperation has been established, it seems important to ask whether this whole process could be more effective, and whether Europe could glean some of the strong points of program management from other industrially-advanced nations. MLnet believes that the following points should be seriously considered for the IT Specific Program within the Fourth Framework Programme: In Long Term Research: % It recommends that the consortium should normally involve only three or four partners. But that greater interchange of ideas should be achieved by having a number of annual contractor-only Research meetings where all projects review their recent results, problems, and longer term goals. Such meetings would essentially be mini- versions of the former ESPRIT week meetings and would be very similar in format to the meetings which the American ONR agency holds (indeed, it might be argued that the Networks of Excellence would be appropriate entities to organize these meetings). % MLnet had major problems with the suggestion that in the 4th Framework, there might be a number of short projects which would test the validity of an idea, so that a subset of these projects might evolve into larger and longer R & D projects. MLnet's views are that this is not an appropriate way to view the relationship between Long Term Research and R & D. By its very nature, Long Term Research addresses fundamental problems in a discipline. In our view, the role of R & D programs/projects is to implement advanced (near market) prototypes from the insights gleaned from Long Term Research actions. In R & D: Again we would advocate that consortia would normally be comprised of four or five partners, where there would be a tight coupling between technique-developers, industrial partners and "end-user" organizations. (We would hope that SMEs or groupings of SMEs would be involved in these consortia.) Again, we suggest that annual meetings be held, involving projects which clearly have related interests and goals, ie those that are active in the same sector. Less frequently, say bi-annually, such meetings should overlap with the relevant Long Term Research Annual meeting. Other points which we would like to raise: % it would be helpful to Research Laboratories, both academic and industrial, if some form of rolling program, or rolling set of program calls could be established. This would enable institutions to plan their workloads more effectively , and more crucially would allow contract Research staff to plan their careers more effectively. % it would be helpful, particularly to University-based groups, if the time between contract announcement and the start date, could be increased to at least four months. (Currently it is often hard to recruit appropriately highly skilled contract staff in the very short lead time). % MLnet would like to see more formal links, particularly at the level of Long Term Research actions, enhanced with Japan and America. % MLnet believes that the Networks of Excellence are an effective mechanism for coordinating information about a sub-discipline within Europe, and very much hope that they will continue to have a role within the 4th Framework. Specifically we believe that MLnet's Industrial Liaison Workshop, Familiarization Workshops and Summer School will all be important for strengthening the community of European Scientists/Technologists working in the areas of ML and KA; this in turn should enhance the competitiveness of the European IT industry. % MLnet believes that NoEs should have an input to the Community's planning of future frameworks and programs. Derek Sleeman Aberdeen 27th January 1994 / revised 15th February *********************************** ECML-94 Community Meeting Francesco Bergadano and Luc de Raedt introduced Derek Sleeman (Academic Coordinator of MLnet) and Lorenza Saitta (Convener of MLnet's Research Committee). Derek Sleeman gave a brief overview of MLnet, what it had achieved in the first year, and what it was planning to do in subsequent years. One of the roles it had assumed was that of organising the annual European Conference on ML. This was essentially coordinated by the Research Committee; he then handed the meeting over to Lorenza Saitta to discuss the planning of ECML-95. Lorenza Saitta reported that in response to her request for nominations for sites for ECML-95 she had received only one response, namely the FORTH Institute at Heraklion in Crete; and that unless there were any strong objections that this would be the site for ECML-95. Vassilis Moustakis then outlined some of the facilities which they could offer at FORTH (new CS Dept Building, wide area network etc). He also explained that hotel accommodation should be both varied and reasonably priced if one avoids Easter Sunday itself. (In subsequent discussions it was agreed that the conference would be held on 25-27 April (1995) with the Familiarisation Workshops being on the afternoon of the 28th and all day on the 29th). Vassilis also pointed out that it should be possible also to get charter flights at this time of the year to Crete; these can be with or without accommodation. The next phase of the discussion was the selection of the Program Chairs. After a relatively short discussion it was agreed that these should be Nada Lavrac (Ljubljana) and Stefan Wrobel (GMD). Given they are both active in ILP they agreed to ensure that other ML specialities are strongly represented in the Program Committee. Derek Sleeman concluded the meeting by proposing a very sincere vote of thanks to this year's Program Chairs, Francesco Bergadano and Luc de Raedt; and to all their support staff. *********************************** ECML-95 8th EUROPEAN CONFERENCE ON MACHINE LEARNING 25Q27 April 1995, Heraklion, Crete, Greece First Announcement and Call for Papers General Information : Continuing the tradition of previous EWSL and ECML conferences, ECML-95 provides the major European forum for presenting the latest advances in the area of Machine Learning. Program : The scientific program will include invited talks, presentations of accepted papers, poster and demo sessions. ECML-95 will be followed by MLnet Fa- miliarization Workshops for which a separate call for proposals is published on page 7 of this Newsletter. Research areas : Submissions are invited in all areas of Machine Learning, including, but not limited to: abduction analogy applications of machine learning automated discovery case-based learning computational learning theory explanation-based inductive learning inductive logic learning programming genetic algorithms learning and multistrategy problem solving learning reinforcement representation revision and learning change restructuring Program Chairs : Nada Lavrac (J. Stefan Institute, Ljubljana) and Stefan Wrobel (GMD, Sankt Augustin). Program Committee : F. Bergadano (Italy) I. Bratko (Slovenia) P. Brazdil (Portugal) W. Buntine (USA) L. De Raedt (Belgium) W. Emde (Germany) J.G. Ganascia (France) K. de Jong (USA) Y. Kodratoff (France) I. Kononenko (Slovenia) W. Maass (Austria) R.L.deMantaras (Spain) S. Matwin (Canada) K. Morik (Germany) S. Muggleton (UK) E. Plaza (Spain) L. Saitta (Italy) D. Sleeman (UK) W. van de Velde (Belgium) G. Widmer (Austria) R. Wirth (Germany) Local chair : Vassilis Moustakis, Institute of Computer Science, Foundation of Research and Technology Hellas (FORTH), P. O. Box 1385, 71110 Heraklion, Crete, Greece (E-mail ecml-95@ics.forth.gr). (Phone +30 81 229 346/302). (Fax +30 81 229 342). Submission of papers : Paper submissions are limited to 5000 words. The title page must contain the title, names and addresses of authors, abstract of the paper, research area, a list of keywords and demo request (yes/no). Full address, including phone, fax and E-mail, must be given for the first author (or the contact person). Title page must also be sent by E-mail to ecml-95@gmd.de. If possible, use the sample LaTeX title page that will be available from ftp.gmd.de, directory /ml-archive/general/ecml-95. Six (6) hard copies of the whole paper should be sent by 2 November 1994 to: Nada Lavrac & Stefan Wrobel (ECML-95) GMD, FIT.KI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany (email wrobel@gmdzi.gmd.de) (Phone +49 2241 14 2670/8). (Fax +49 2241 14 2889) Papers will be evaluated with respect to technical soundness, significance, originality and clarity. Papers will either be accepted as full papers (presented at plenary sessions, published as full papers in the proceedings) or posters (presented at poster sessions, published as extended abstracts). System and application exhibitions : ECML-95 offers commercial and academic participants an opportunity to demonstrate their systems and/or applications. Please announce your intention to demo to the local chair by 24 March 1995, specifying precisely what type of hardware and software you need. We strongly encourage authors of papers that describe systems or applications to accompany their presentation with a demo (please indicate on the title page). Registration and further information : For information about paper submission and program, con- tact the program chairs (E-mail ecml-95@gmd.de). For information about local ar- rangements or to request a registration brochure, contact the local chair (E-mail ecml-95@ics.forth.gr). Important Dates : Submission deadline : 2 November 1994 Notification of acceptance : 13 January 1995 Camera ready copy : 9 February 1995 Exhibition requests : 24 March 1995 Conference : 25 Q 27 April 1995 *********************************** MLnet Sponsored Familiarization Workshops, Heraklion, Crete April 28-29, 1995 MLnet is planning to hold a series of Technical Workshops together with some other infrastructure discussions, on 28-29 April 1995, ie, immediately after the ECML95, which will also be held in Heraklion. Proposals are requested for these technical workshops. Such proposals should include: % A detailed discussion of the subarea(s) to be covered. % Names and addresses of the proposed Program Committee (together with a note indicating whether the named people have agreed to act). % Names of possible invited speakers and some indication of their travel costs. % Some indication of the form of the workshop (ie, split between presentations and panels etc). This information should be sent to Derek Sleeman preferably by regular mail, to arrive by 8 August 1994. This item will be discussed at the September Management Board meetings, and proposers will be informed of the decision by mid September. Derek Sleeman will be happy to discuss possible topics with potential organisers. NB: MLnet expects to have sufficient funds to support the travel to Crete and the subsistence at the Familiarization Workshop, for several members of each MLnet node. *********************************** MLnet Workshop on Industrial Applications of Machine Learning Dourdan, France, September 2 and 3, 1994 Organizer: Yves Kodratoff (LRI & CNRS, University of Paris-South, Orsay, France). Program Friday, September 2: Overview presentations % Ivan Bratko (JSI, Ljubljana, Slovenia) - On the state-of-the- art of industrial applications of ML % Gregory Piatetski-Shapiro (GTE, USA) - DDB: data mining in data bases % Wilfried Achthoven (Bolesian Systems, NL) - The state of the art in knowledge acquisition: industrial practice with KADS, machine learning and case-based reasoning % Attilio Giordana (Univ. Torino, Italy) - Applications of machine learning to robotics % Franz Schmalhofer (DFKI, Germany) - Unifying KA and ML for applications % Vassilis Moustakis (Univ. Crete, Greece) - An overview of applications of ML to medicine Saturday, September 3: Special address: Setsuo Arikawa (Kyushu University, Japan) - Knowledge acquisition from protein data by machine learning system BONSAI Results of ESPRIT projects: % Nick Puzey (BAE, UK) - Industrial applications of MLT % Pavel Brazdil (Univ. Porto, Portugal) - Industrial applications of STATLOG % Attilio Giordana (Univ. Torino, Italy) - The results of BLEARN Reports on results: % Gend Kamp (Univ. Hamburg, Germany) - Applications of case-based reasoning % Pieter Adriaans (Syllogics, NL)- Application of GAs at Syllogics % Fabio Malabocchia (CSELT, Italy) - Machine learning at CSELT % Jrgen Herrmann (Univ. Dortmund, Germany) - Learning rules about VLSI-design % Reza Nakhaeizadeh (Daimler-Benz, Germany) - Machine learning at Daimler-Benz % Franois Lecouat (Matra-Espace, France) - Case-based reasoning at Matra-Space Demos will take place during the evenings. Participants are welcomed to also attend the ML Summer School at Dourdan on the following week (September 5-10). Note that a separate registration will be required for the meeting. The topics presented in the first two days of the Summer School will be of greatest relevance to people attending the workshop. Registration fee The registration is 800 FF. Some full and partial grants for travel, registration and accommodation are available for European students and researchers. Applicants must send a letter stating their motivations and a CV before June 26. Requests for information and registration forms (see central page of this Newsletter) are to be addressed to Dolores Caamero, (MLSS'94), LRI, B t. 490, Universit Paris-Sud, F-91405, Orsay Cdex, France (e-mail: mlss94@lri.fr). *********************************** MLnet Summer School on Machine Learning and Knowledge Acquisition Dourdan, France, September 5-10, 1994 The summer school is organized by Cline Rouveirol (LRI, University of Paris-South, France). Its aim is to provide training in the latest developments in Machine Learning and Knowledge Acquisition to AI researchers, and also to industrials who are investigating possible applications of those techniques. The school will be held in Dourdan (some 50 Km south of Paris). It is sponsored by CEC through the MLnet Network of Excellence (Project 7115), and PRC-IA (Projet de Recherche Coordonn, groupe Intelligence Artificielle). Program Monday Sept 5th . Morning: Case-Based Reasoning (Agnar Aamodt, Univ. Trondheim, Norway) [3h] . Afternoon: Learning and Probabilities (Wray Buntine, RIACS/NASA Ames, Moffet Field, CA, USA) [3h] Tuesday Sept 6th . Morning: Learning and Noise (Ivan Bratko, JSI, Ljubljana, Slovenia) [3h] . Afternoon: Knowledge Acquisition (Bob Wielinga, Univ. Amsterdam, NL) [3h] Wednesday Sept 7th . Morning: Integrated Architectures (Lorenza Saitta, Univ. Torino, Italy) [3h] . Afternoon: Knowledge Revision (Derek Sleeman, Univ. Aberdeen, UK) [2h], (Stefan Wrobel, GMD, Bonn, Germany) [2h] Thursday Sept 8th . Morning: Knowledge Acquisition and Machine Learning (Maarten van Someren, Univ. Amsterdam, NL) [3h] . Afternoon: Reinforcement Learning (L.P. Kaelbling, Brown Univ., USA) [3h] Friday Sept 9th . Morning: Inductive Logic Programming (S. Muggleton, Univ. Oxford, UK) [3h] . Afternoon: Inductive Logic Programming (C. Rouveirol, LRI, Univ. Paris-Sud, France) [2h], (F. Bergadano, Univ. Catania, Italy) [2h] Saturday Sept 10th . Morning: Conceptual Clustering (G. Bisson, LIFIA, Grenoble, France) [3h] Invited seminars and demonstrations of software will be organized during the evenings. Registration fee Some full and partial grants for travel, registration and accommodation can be accorded to European students and researchers and to members of PRC-IA. Applicants must send a letter stating their motivations and a CV before June 26. Requests for information and registration forms (see central page of this Newsletter) are to be addressed to Dolores Caamero, (MLSS'94), LRI, B t. 490, Universit Paris-Sud, F-91405, Orsay Cdex, France. E-mail: mlss94@lri.fr. *********************************** Technical Reports on the Catania Familiarization Workshops Report on WS1: Theory Revision and Restructuring in Machine Learning by Stefan Wrobel Organizer: Stefan Wrobel (GMD, Sankt Augustin, Germany) Program Committee: Hilde Ade, Carl-Gustaf Jansson, Stefan Wrobel. 1. Workshop Topic With the growing complexity of applications being tackled by Machine Learning, the field has become increasingly aware that besides approaches for the initial acquisition of knowledge bases we also need techniques for theory revision and restructuring, i.e., techniques that can use existing learned or human-supplied domain theories and can modify them to improve their correctness, completeness, efficiency or understandability. Traditionally, this topic has been examined in different contexts. Revision has always been a part of incremental or hill-climbing learning systems which keep only one current hypothesis and modify it whenever new examples arise. More recently in ILP, revision has been identified has an important part of approaches that learn multiple predicates simultaneously, and incorporated as a central component of integrated multi-strategy learning systems. Under the name of refinement, the expert systems community has studied ways of improving system knowledge bases over time. Finally, revision and restructuring are also important topics in neighbouring fields of ML, such as knowledge representation, logic programming or deductive databases. The goal of this familiarization workshop was to bring together the various approaches to revision and restructuring, that are currently being pursued, both to allow participants to learn about each others work and to see if a common framework is emerging. The orientation of almost all the 12 presentations held at the workshop reflects a strong trend towards a first-order clausal logic framework as a common basis, paralleling the current focus on ILP in Europe. In this framework, revision is seen as the task of changing a theory by generalizing it when positive examples are erroneously not covered, and by specializing it when negatives examples are erroneously covered. 2. Overview of the Presentations The presentations in the morning each took a particular perspective on this general framework. In the presentation by Hilde Ade, Bart Malfait, and Luc De Raedt ("RUTH: an ILP Theory Revision System"), the general framework was extended by allowing not only facts as positive and negative examples, but also full clauses that are then interpreted as integrity constraints. Francesco Bergadano and Daniele Gunetti ("Intensional Theory Revision") showed how intensional evaluation of clauses for revision can be made tractable by assuming the initial program is almost correct and using a strong search bias (clause sets). Stefan Wrobel ("Heuristic Control of Minimal Base Revision in KRT Using a Two-Tiered Confidence Model") concentrated on the specialization subtask and showed how heuristic evaluation functions can be used to select among the possible minimal revisions of a theory. Whereas Wrobel's KRT system produces minimal specializations with exception sets, Henrik Bostroem and Peter Idestam-Almquist ("Specialization of Logic Programs by Pruning SLD- Trees") showed an approach that needs only standard definite clauses for (non-minimal) specialization by unfolding. In a second session, three approaches to revision were presented that each use different biases to control the revision process. Marco Botta ("KBL-2: A First Order Theory Refiner") showed how a data structure called an LT-tree is used to represent all the information necessary for revision of the theory. While essentially equivalent to an AND-OR derivation tree, substitution information in an LT-tree is computed bottom-up and kept in a database system. Floriana Esposito, Donato Malerba, and Giovanni Semeraro ("INCR/H: A system for Revising Logical Theories") departed from the standard framework by considering a different notion of subsumption (theta-subsumption under object identity) which produces more natural, but no longer unique, LGGs. They presented a specialization operator for linked clauses that is shown to be minimal under this definition of subsumption. Finally, Filippo Neri ("An Approach to Knowledge Refinement and Theory Revision") gave an overview of theory revision processes in the WHY system, which is characterized by its separation of the knowledge base into causal model and phenomenological theory that are treated differently during revision. In the afternoon session, the orientation of talks was more varied. In the only talk on the restructuring side of the workshop, Edgar Sommer ("Rule Base Stratification: An approach to theory restructuring") showed how a so-called stratification algorithm can be used to introduce intermediate concepts into a knowledge base, and used an example knowledge base to show that this can greatly increase understandability. Alipio Jorge and Pavel Brazdil ("Incrementality issues in Sketch Refinement") used a surprising notion of refinement, not referring to improvements of a theory, but to the instantiation process of a sketch when turning it into a final hypothesis. Yutaka Sasaki, Masahiko Haruno, and Shigeo Kaneda ("Grammar Rule Revision by Rephrasing Unparsable Sentences") then presented a revision approach embedded into a natural language parsing system for Japanese. They showed that their revision method improved parsing capabilities on a simple test set compared to the method previously used. Daniel Borraja and Manuela Veloso ("Multiple Target Concept Learning and Revision in Non-linear Problem Solving"), in a continuation of their main program talk, showed how revision procedures can be used to keep up to date conditions that recommend operators in particular situations of a planning problem. The last talk of the workshop by Susan Craw, Derek Sleeman, Robin Boswell, and Leonardo Carbonara ("Is Knowledge Refinement Different from Theory Revision?") turned out to be an ideal starting point for a discussion about the terminology that is being used in the field to describe the different tasks. In the end, a hierarchy of tasks ended up on the blackboard that regarded refinement as the most general term, subsuming revision (modifying an incorrect or incomplete theory) and restructuring (modifying a correct and complete theory to improve other properties like understandability). Theory revision in turn features generalization and specialization (debugging) as subtasks, whereas restructuring consists of both performance enhancement (using EBL, partial evaluation) and understandability enhancement (using operators for introducing new predicates). (theory/knowledge) refinement | QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ | | revision restructuring | | QQQQQQQQQQQQQQQQQ QQQQQQQQQQQQQQQQQQQQQ | | | | specialization generalization performance understandability (debugging) (EBL, PE) In summary, especially because of the final discussion, I believe the workshop has served to clarify a little bit the terminology used in the field, and presented a lot of work on one part of the spectrum, namely revision. For the future, it seems that restructuring should be more of a focus, particularly given the importance of understandability noted in the main conference by Yves Kodratoff and Lorenza Saitta. P.S. As announced at ECML, the proceedings (extended abstracts) of this workshop are available via FTP and will appear as a GMD technical report: "Proc. MLnet Familiarization Workshop on Theory Revision and Restructuring in Machine Learning (at ECML-94, Catania, Italy), ed. Stefan Wrobel, Arbeitspapiere der GMD, GMD, Pf. 1316, 53754 Sankt Augustin, Germany, 1994. Available via FTP from ftp.gmd.de as /ml- archive/MLnet/Catania94/theory-revision.ps.gz." If you cannot access FTP, or would like a printed copy, send mail to stefan.wrobel@gmd.de. *********************************** Technical Reports on the Catania Familiarization Workshops Report on WS2: Knowledge Level Models of Machine Learning by Walter Van de Velde Organizer: Walter Van de Velde (AI Lab, Vrije Universiteit Brussels) Program Committee: Agnar Aamodt, Dieter Fensel, Enric Plaza, Walter Van de Velde and Maarten Van Someren. 1. Workshop Topic The aim of this workshop was to discuss knowledge level modeling applied to machine learning systems and algorithms. An important distinction in current expert systems research is the one between knowledge level and symbol level [Newell, 1982]. Systems can be described at either of these levels. Briefly stated, a knowledge level description emphasizes the knowledge contents of a system (e.g. goals, actions and knowledge used in a rational way) whereas the symbol level describes its computational realization (in terms of representations and inference mechanisms). There is a consensus that modeling at the knowledge level is a useful intermediate step in the development of an expert system [Steels and McDermott, 1993]. So called second generation expert systems explicitly incorporate aspects of their knowledge level structure, resulting in potential advantages for knowledge acquisition, design, implementation, explanation and maintenance (see [David et al., 1993] for an overview on the state of the art). The technical goal is to construct generic components which can be reused and refined as needed, guided by features of the domain and the task instead of by software engineering considerations. This workshop investigated the results of describing learning systems at the knowledge level, hoping to gain some of the same advantages that were experienced with second generation expert systems. Although the earliest attempts to do this [Dietterich, 1986] failed to lead to useful results, later efforts provided interesting insights [Flann and Dietterich, 1989]. Maybe a more important reason for the exploration of the knowledge level of learning systems is that the notion of knowledge level itself, as it is currently used in expert systems research, is no longer equivalent to Newell's [Van de Velde, 1993]. Currently the knowledge models used are considerably more manageable, structured and, in a sense, more engineering oriented. Knowledge level analysis of learning systems can directly benefit from the developments in knowledge modeling that are currently taking place (see e.g. [Klinker, 1993] for recent work). Moreover the knowledge level analysis of machine learning systems can be done directly in available environments allowing for the easy integration with problem solving or knowledge acquisition systems. Note that the relevance of the knowledge level ideas to machine learning is broader than what is described here (e.g., learning of knowledge level models). To keep the present workshop relatively focussed it was suggested to stick closely to the main topic: knowledge level modeling of machine learning. This topic is relevant for several reasons. It provides insights into essential features, differences and similarities of machine learning algorithms. It contributes to the flexible and problem specific configuration of learning systems, and their integration with performing systems. Knowledge level analysis of learning systems also enables the exchange and reuse of results in machine learning and knowledge acquisition, which is one of the main bottlenecks in current research practice. The topic of knowledge level modeling of machine learning is also well suited for an MLnet familiarization workshop. Europe has a strong tradition in knowledge level modeling, with the developments of such methodologies as KADS and Components of Expertise, and of languages and environments for constructing knowledge level models (KARL, MoMo, KresT, FML, and so on) and large scale projects such as MLT and aspects of KADS-II. The workshop attempted to be a bridge between knowledge acquisition and machine learning, using concepts of the KA community to understand results in the ML community. 2. Overview of the Presentations The invited talk ("Is a knowledge-level characterization of robotic learning always possible?") was presented by Luc Steels (VUB AI-Lab). In his well known entertaining fashion he challenged the scope of the task that the workshop had set itself. That knowledge level descriptions of problem solving are possible, Luc Steels would be the last to deny. That such descriptions of learning are feasible and interesting he would not dispute. But that knowledge level modeling of robotic behaviour is useful or possible, is less clear. Luc's presentation generated some confusion, as might be expected. How is his notion of 'motivation' different from goals? Isn't he just doing control theory? Maybe Franz Schmalhofer and Stuart Aitken (DFKI, Kaiserslautern, Germany) could have clarified some of these issues..., if only they had been there. Their paper ("Beyond the knowledge level: Behaviour descriptions of machine learning systems") attempts to combine knowledge level modeling with three other types of models to capture aspects of skill and performance, rather than just competence. The presentation by Celine Rouveirol (LRI, Paris) and Patrick Albert (ILOG) addressed a central issue of the workshop. "Knowledge Level Modelling of Generate and Test Learning Systems" covers more than this title suggests. What knowledge is involved in the life-cycle of a learning task? Tasks for selecting examples, representations, algorithms and their parameters, evaluating test results and so on. This project aims at building a configurable learning environment that assists in these decisions. The next presentation approached the same problem in a complementary way. Aurelien Slodzian (VUB AI-Lab, Brussels), presented a concrete experiment on applying knowledge level modeling to learning. He described how, within the knowledge engineering workbench KresT, a wide class of decision tree learning algorithms can be modeled, configured, operationalized and integrated. The experiment leaves little doubt about the feasibility. KresT allows one to put into place models of problem solving, learning and the meta-reasoning that may be involved in selecting and controlling these. Some of the details of the actual methods and knowledge for configuration and selection still have to be filled in, but the project provides all the hooks to store and operationalize them. Wouldn't it be great to use the machine learning models from Celine and Patrick within the LearnKit of KresT? The paper by James Cupit, Nigel Shadbolt and Sean Wallis from the University of Nottingham ("The application of a knowledge acquisition methodology to the analysis of large databases") describes a specific methodology for analysing large data-sets. What makes this particularly relevant is that they use a model of analysis to direct the process of constructing and revising conceptual expertise models. The model is also used to access various tools that can be instrumental in the process. Enric Plaza and Luis Arcos' (IIIA, Blanes, Spain) presentation on the reification of learning methods in NOOS was cancelled. This was unfortunate, because NOOS is one of those systems that takes a knowledge level analysis of case-based reasoning methods as the basis for a computational architecture. Progress along those lines provides primary data points to assess the practical value of the enterprise. Dieter Fensel (AIFB, University of Karlsruhe) asked the question 'What makes it difficult to apply Machine Learning in Model-Based Knowledge Acquisition?' It seems that machine learning has not followed the shift in knowledge acquisition from direct extraction of knowledge in operational form (e.g. rule or frame) to knowledge level modeling. So, it seems that there is a growing gap between what machine learning can produce and what knowledge acquisition needs. To bridge this gap, Dieter argued, several research topics must be addressed (and occasionally they are). How can knowledge level description be used to support selection, modification, combination, and creation of machine learning techniques related to given learning tasks? How can a complex learning task be decomposed? How can one type be used to guide the acquisition of others? How can the bias of a machine learning technique be represented at the knowledge level? Can formal languages, claimed to be highly successful for models of problem solving be used for this purpose? The workshop ended where it had started. Agreeing that knowledge level descriptions are not all there is, and asking what can they be used for and what is needed in addition to account for a full theory of learning behaviour? No doubt this will be an issue for a follow up workshop that is already in the course of being organized. Acknowledgements This workshop was organized with the help of Agnar Aamodt (University of Trondheim, Norway), Dieter Fensel (University of Karlsruhe, Germany), Enric Plaza (IIIA, Blanes, Catalunya, Spain), Walter Van de Velde (VUB AI-Lab, Brussels, Belgium) and Maarten Van Someren (SWI, University of Amsterdam, The Netherlands). Thanks to MLnet for the overall organisation and support to the familiarization workshops. Some of the papers are available on the World-Wide Web at URL http://arti.vub.ac.be/~walter/ECML/kl-ml.html References [David et al., 1993] David, J.-M., Krivine, J.-P., and Simmons, R. (Eds.). (1993). Second Generation Expert Systems. Springer Verlag, Berlin. [Dietterich, 1986] Dietterich, T. G. (1986). Learning at the knowledge level. Machine Learning, 1, 287-316. [Flann and Dietterich, 1989] Flann, N. and Dietterich, T. (1989). A study of explanation-based methods for inductive learning. Machine Learning, 4(2), 187-226. [Klinker, 1993] Klinker, G. (Ed.). (1993). Special Issue: Current issues in knowledge modeling, volume 5 of Knowledge Acquisition. Academic Press. *********************************** Technical Reports on the Catania Familiarization Workshops Report on WS3: Declarative Bias by Celine Rouveirol Organizer: Celine Rouveirol (LRI, Orsay, France) Program Committee: F. Bergadano, F. Esposito, N. Lavrac, I. Mozetic, C. Nedellec, E. Plaza, L. Popelinsky, D. Sleeman, T. Van de Merckt, M. Van Someren. Seven papers were presented at the workshop; these were all quite diverse. Three papers came from the Inductive Logic Programming community, which has had for a long time a strong interest in making the bias explicit in empirical concept learning. H. Ade, L. De Raedt, M. Bruynooghe's paper "Declarative Bias for bottom up systems", presented a comparative study of language bias effects in bottom up ILP generalisation systems. The paper of M. Grobelnik : "Declarative Bias in the MARKUS system" also presented some language biases for a top-down MIS like system. D. Mdalenic "Declarative Bias in the ATRIS rule induction shell" gave an overview and comparison of a range of search strategies within a hypothesis space. An ILP related paper by Kukolva and Popelinsky investigates the use of the INDEX system of P. Flach in extracting "relevant" constraints that are verified in a relational database. T. Van de Merckt gave an overview of commonly used language and search biases in some Similarity Based Learning systems, more precisely in Instance Based, Neural Networks and Decision Tree learning. J. Herrmann elaborated on biases in his multi strategy system COSIMA, that was also presented with a different focus at ECML-94. F. Neri developed his view of bias in an incremental learning system, where the order in which the examples are provided to the system can be seen as a major factor that influences learning. The presented papers, together with the discussions during the workshop and at the closing session, demonstrated that there is currently a substantial effort to find a unifying framework to compare the different learning biases used in learning systems. Some open questions that were raised are: Is there some classification of biases that can be discussed independently from specific learning systems? What are the biases that should be made delarative in a learning system (that is, shiftable, automatically or partially with the help of a user)? How can the effects of a given bias be characterized with respect to a ML system performance? Some answers to the latter questions were provided in the workshop , especially in the comparison papers of Ade et al, Mdalenic, Decastaeker and Van de Merckt. Some recent results about the complexity of learning under different language biases produced by the ML and PAC communities have also contributed to an understanding of these issues. To sum up, the issue of Declarative Bias seems central to the successful use of learning. The area of research is quite vast, and there is still no widely agreed common classification of biases, nor a clear understanding of how particular biases effect the learning outcome. But the workshop showed that more and more effort is currently focusing on finding unifying frameworks to study, represent and experiment with biases for families of comparable learning systems. The working notes of the workshop are available upon email request to celine@lri.fr. *********************************** Technical Reports on the Catania Familiarization Workshops Report on WS4: Machine Learning and Statistics by Gholamreza Nakhaeizadeh Organizer(s): Gholamreza Nakhaeizadeh (Daimler-Benz, Ulm) and Charles Taylor (University of Leeds) Program Committee: Hans-Hermann Bock, Georg Bol, John Hand, Bob Henery, Igor Kononenko, Jrgen Kreuziger and Rafael Molina 1. Workshop Topic Statistics and Machine Learning (ML) can help each other's development. On one hand, the statistical and probabilistic approaches can be applied by ML-researchers in developing different ML algorithms. In this connection, one can mention the concepts developed originally by statisticians and which have been applied by the ML-community to optimize, prune, and especially to evaluate different ML-algorithms. Concepts like probabilistic decision trees and causal networks can be seen in this connection as well. On the other hand, some ML algorithms, which can perform classification and forecasting tasks, were developed originally by the ML-Community. They are, however, of interest to statisticians and can demonstrate new possibilities in adapting classical statistical approaches to enable the handling of real-world applications. In this connection, one can mention some of the decision tree and rule based algorithms, as well as the Case-Based Reasoning approach. Furthermore, perhaps the ML community can adapt some statistical algorithms to become ML algorithms. The existing barrier which prevents the statistical algorithms from becoming ML algorithms is that the partition regions can not (yet) be described in a way that is meaningful to, and evaluable by humans. For example, the k-NN method might lend itself here, if the decision regions could be approximated by a small number of Voronoi polygons, and the centre of these cells then termed as prototypes for use in classification. The above facts show that the two communities, Statistics and ML, can learn a lot from each other. The main aim of this workshop has been to bring ML-researchers and statisticians together to discuss the different aspects of the interface between ML and Statistics. These *********************************** Focus on Machine Learning Research at Daimler-Benz by Jutta Stehr Daimler-Benz is often still seen as a car manufacturing company; probably not so well known are its various research activities in the area of computer science and information technology. Most of the IT research departments are located at the Daimler-Benz Research Centre in Ulm and in Berlin. One of the Ulm groups is concerned with Machine Learning and it will be briefly introduced here. Research in an industrial company tends to be more constrained than academic research in terms of time and goals. Research projects often focus on specific real-world problems and the results should be either easily incorporated into the company's products (in the case of Daimler-Benz IT research this is software for cars, aeroplanes, trains, and industrial machinery) or should contribute to various industrial processes like engineering, manufacturing, or marketing. Because of this background the research activities within the ML group cannot be confined to basic research alone. Rather, the group is concerned with bridging the gap between research and industrial real-world problems. Therefore a principle thrust of the work is to develop software solutions by using advanced data analysis and Machine Learning algorithms to meet the needs of industry without being directed to mere application development or commercial products. The ML group is part of the department for Software Engineering Research (Manager: Wolfgang Hanika) and its basic roots lie in the fields of Statistics and Inductive Learning. The leader of the group, G. Nakhaeizadeh, is Professor of Econometrics at the University of Karlsruhe. He has been involved in statistical ML research for several years; this spans work at the University of Karlsruhe and within Daimler-Benz. The activities of the group cover a broad range of ML topics. However, research divides into three major areas: (i) application of ML algorithms to fields like image processing, text and speech understanding, and quality insurance, (ii) development of ML systems in various domains of financial engineering, logistic and production planning. (iii) evaluation and tuning of ML algorithms. The ML group currently includes 3 permanent members at the Daimler- Benz research centre, with 3 PhD students at Ulm and another 2 at Karlsruhe, and a couple of students involved in master level research. There were several internal cooperation with other departments in the last years. For example, together with the Marketing Department of Mercedes-Benz the possibilities of the application of Machine Learning to the prediction of trucks' market development was investigated. Another project was in the area of quality insurance: here the test of automatic transmissions was investigated by automatically generating rules from statistical data. Beside the internal projects the ML group has been involved in the Esprit project StatLog. Daimler-Benz has directed the project which had the overall aim to give an objective assessment of the potential of different classification algorithms in solving significant commercial and industrial problems. 23 algorithms including ML- and statistical algorithms, and Neural Nets were tested on about 22 large-scale problems. The participants developed a set of objective criteria for comparing classification algorithms and established an interactive test environment (the public domain software packages "Evaluation Assistant" and "Application Assistant"). Current work include: % development of an intelligent Credit Scoring System that will be the basis of a risk management concept. Emphasis is laid upon incremental learning, the use of cost functions, and the comparison of Machine Learning Technology, Neural Networks, and Statistics (Karl Dbon, Gholamreza Nakhaeizadeh). % development of a hybrid forecasting and classification method by combining several Machine Learning Strategies and different types of inference including Neural Networks, Case based Reasoning and Statistical Methods (Stefan Ohl, Gholamreza Nakhaeizadeh). % application of ML algorithms to short-term and medium-term exchange rate prediction. A system will be implemented which uses symbolic ML algorithms and Neural Networks as well as ARIMA-Models and econometric techniques for forecasting non stationary, economic time series (Elmar Steurer). % evaluation of Case Based Reasoning techniques to support real-world planning and design tasks. The work tends to focus on the practicability of CBR in engineering and manufacturing environments (Jutta Stehr). % evaluation of the applicability of ML technology, especially incremental learning techniques to office environments (Udo Grimmer). This project includes a proposed cooperation with Washington State University on the application of ML algorithms to form filling. Other projects are on genetic algorithms for industrial-size classification problems, on hybrid forecasting methods for economic time series including k-nearest neighbour method, Neural Networks and Regression Trees, and on the comparison of Symbolic and Statistical ML- Algorithms. Selected Publications: % Graf, J. and Nakhaeizadeh, G. (1991): Application of Statistical and Connectionist Systems for predicting the Development of Financial Markets. In Heilmann, W.R. et al. (eds.): Geld, Banken und Versicherungen, VWW Karlsruhe, pp. 1705 - 1720. % Graf, J. (1992): Stock Market Prediction with neural networks. In Gritzmann, R. et al. (eds.): Operations Research '91, Tagungsband 1992, pp. 496 - 499. % Graf, J. (1992): Long-Term Stock Market Forecasting using Artificial Neural Networks. In Novak, M. (eds.): Neural Network World, Volume 2, No. 6, pp. 615-620. % Graf, J. and Nakhaeizadeh, G. (1993): Neural Nets and Symbolic Machine Learning Algorithms to Prediction of Stock prices. In Plantamura, V. et al. (eds.): Logistic and Learning for Quality Software Management and Manufacturing. % Graf, J. and Nakhaeizadeh, G. (1993): Recent development in Solving the Credit Scoring Problem. In Plantamura, V. et al. (eds.): Logistic and Learning for Quality Software Management and Manufacturing. % Knoll, U. Nakhaeizadeh, G. and Tausend, B. (1994): Cost-sensitive Pruning Methods for Decision Trees. In Bergadano, F. and De Raedt, L. (eds.) Proceedings of the Eight European Conference on Machine Learning (ECML-94). Springer Verlag. % Merkel A. and Nakhaeizadeh, G. (1992): Application of Artificial Intelligence Methods to Prediction of Financial time Series. In Gritzmann, P. et al (eds.). Operations Research 91, pp. 557-559. % Nakhaeizadeh, G. (1992): Inductive Expert Systems and their application in Statistics. In Faulbaum F. (ed.) SoftStat 91. Advances in Statistical Software. Gustav-Fischer, pp. 31-38. % Nakhaeizadeh, G. (1992): Application of Machine Learning to solving industrial problems. In Gritzmann, p. et al (eds.). Operations Research 91, pp. 560-563. % Nakhaeizadeh, G. (1993): Application of Machine Learning in Finance. In: Kirn, S. and Weinhardt, C. (eds.). KI-Methoden in der Finanzwirtschaft, Fachtagung fr KI, Berlin. pp. 137- 142. % Nakhaeizadeh, G. (1994): Learning Prediction of Time Series. A Theoretical and Empirical Comparison of CBR with some other Approaches. In Richter, M. et al (eds.)Proceedings of EWCBR-93, Universitaet Kaiserslautern. % Nakhaeizadeh, G. and Reuter A. (1994): Application of Machine Learning to Predicting Activities in the Automobile Market. To appear in Langley, P. (ed.): Fielded Applications of Machine Learning, Morgan Kaufmann. % Steurer, E. (1993): Nonparametric Exchange Rate Prediciton by using a modified Nearest Neighbour Method. In Refenes, A. N. et al. (eds.): Neural Networks in the Capital Markets, Proceedings, London Business School % Steurer, E. (to appear): Application of Chaos Theory to Predicting the Development of Exchange Rates. In: Hipp, Christian et al. (eds.): Tagungsband zur 6. Tagung Geld, Finanzwirtschaft, Banken und Versicherungen, Karlsruhe For further information contact: Gholamreza Nakhaizadeh Daimler-Benz AG Department F3W P.O. Box 2360 89013 Ulm Germany Tel: +49 731 505-2860 Fax: +49 731 505-4210 email: reza@fuzi.uucp *********************************** Funding for Workshops/Meetings MLnet is happy to receive proposals for European based workshops in the areas covered by the Network re Machine Learning; Knowledge Acquisition; Case Base Reasoning etc. To be eligible the meeting must be international in character. Generally support is only available from MLnet for helping to organise the meeting. (Organisers can apply separately to the EC for other funds to support the academic/technical parts of the program.) For further details please contact: Derek Sleeman (Academic Coordinator) Fax: + 44 224 273422 email: (sleeman or mlnet) @csd.abdn.ac.uk Lorenza Saitta (Convener of Research Technical Committee) Fax: + 39 11 751 603 email: saitta@di.unito.it *********************************** Guidelines for Proposals to MLnet % The proposal should specify clearly the tasks to be done. % It should clearly specify the outcomes to be delivered with appropriate time scales % All resources requested (equipment, staff support, phones etc.) should be justified. Also note % Given the terms of the EC contract, no overheads can be charged. % Aberdeen are only able to pay cheques in either UK pounds or ECUs (any other currency will cause us major problems as the EC contract does not allow the Univ. of Aberdeen to charge bank charges). *********************************** Putting Together a Machine Discoverer: Basic Building Blocks* Jan M. Zytkow Whichita State University 1845 North Fairmont Wichita KS 67208-1595 USA zytkow@wise.cs.twsu.edu 1. Main Direction of Machine Discovery Machine discoverers can be briefly defined as computer systems that autonomously pursue knowledge. Research in machine discovery has been growing in two main directions: (1) automated scientific discovery, and (2) knowledge discovery in databases. Both directions differ in the search techniques used and the expected results of discovery. Database applications are focused on data collected for purposes different than discovery, and typically sparse as a source of information about real world phenomena. In contrast, scientific applications of machine discovery include fine data generation as a part of the discovery process. Knowledge discovery in databases has been described in several collections of papers, edited by Piatetsky-Shapiro (1991, 1993), Piatetsky-Shapiro & Frawley (1991), Zytkow (1992), Ras (1993), Ziarko (1993), and many other papers. Automated scientific discovery is primarily concerned with reconstruction of discovery mechanisms in sciences such as physics, chemistry and biology. Many of the recent results can be found in collections edited by Shrager & Langley (1990), Edwards (1993) and Zytkow (1992, 1993). This research can be further split into automated discovery of empirical laws and discovery of hidden structure. We will focus on automated discovery of empirical laws and on a long term goal to build automated robotic discoverers, who develop theories of the real world through empirical investigation. The other branch, discovery of hidden structure, is not considered in this paper. It would require an independent substantial treatment. Contributors include Langley, Simon, Bradshaw & Zytkow (1987); Rose & Langley (1986); Rose (1989); Karp (1990); Valdes-Perez (1990); Kocabas (1991); Fischer & Zytkow (1992); Valdes-Perez, Zytkow, & Simon (1993). The qualitative models approach also belongs here. Contributors include Sleeman, Stacey, Edwards & Gray (1989); Roverso, Edwards & Sleeman (1992); Gordon (1992); Metaxas (1993). 2. Cognitive Autonomy of a Discoverer Throughout history, human discoverers have had to rely on their own judgement, because the knowledge they proposed was new, often contradicting the accepted beliefs. A discoverer can be characterized by autonomous pursuit of new knowledge, accomplished by own choices in the repertoire of discovery techniques and results. We want to put the same qualities into machine discoverers. Difficulties with directly programming knowledge into AI systems led to a widespread belief that intelligent systems should learn by imitation of human learning. After many years of research on machine learning, however, we are still far from an integrated learning mechanism which would do the job. The areas of greatest strength of machine learning, such as concept learning from examples and clustering, are of little use in learning scientific knowledge. It becomes clear that efficient knowledge acquisition requires an agent far more active and autonomous than current learning systems. For instance, a good learner must have a broad understanding of various forms of knowledge. It must also know how to link new pieces of knowledge to its previous knowledge. Such links are typically missing in instruction. Human learners need surprisingly little instruction. Through the shortfalls of machine learning we come to appreciate the autonomous absorption of knowledge by a good learner. Good learners are discoverers; hence understanding the discovery process is fundamental for understanding learning. Research in machine discovery, by its focus on cognitive autonomy and automation of many cognitive steps, is critical for understanding automated knowledge acquisition. Symptomatically, the motivation for the workshop on Learning in Autonomous Agents at the MLnet meeting in Blanes reads like another case for machine discovery (Van de Velde, 1993). There must be profound reasons why we are discoverers. Certainly we were discoverers before we became learners. Otherwise we would not discover the purpose of the learning situations, such as the pointing gesture by which, as infants, we are taught the meaning of our first words. Discovery must also dominate learning in animals, as given the limitations in their language and culture, they must acquire much of their knowledge by discovery. To be useful in our research on machine discovery, the notion of autonomy requires clarification. Suppose that agent A discovers some piece of knowledge K, which is already known to others, as is often the case with our machine discoverers. Agent A can be considered a discoverer of K, if A did not know K earlier and did not learn about K from external sources. It is relatively easy to trace the external guidance received by a machine discoverer because all details of the software are available for inspection. It is true that existing machine discoverers lack autonomy in many ways. They would not make discoveries if humans did not provide help by setting system parameters, selecting search strategies, preparing input data, and providing them with empirical systems with which to experiment. These breaches in autonomy do not disqualify machine discoverers, however, because they are also characteristic of even the greatest human discoverers, who make relatively small steps beyond their inherited background of knowledge and method (Zytkow, 1993). Existing systems are autonomous only to some degree, but future research in machine discovery will increase their cognitive autonomy. An agent becomes more autonomous as it is able to make more choices, satisfy more values and investigate a broader range of goals. Overcoming the individual limitations of autonomy is a big challenge. The mere accumulation of new methods, however, does not suffice. The methods must be strongly integrated, so that more discovery steps can be performed in succession without external intervention. When external intervention is replaced by automated search, which must stay within tractable limits, the accumulation of discovery steps becomes an even bigger challenge. But it provides motivation and opportunity for asking the right research questions and gives the perspective necessary for the answers. A single cognitive step rarely permits a sound judgement about the results. A combination of steps provides more informed reasons for acceptance. 3. Anatomy of a Discoverer The goal of empirical discovery is to develop the theories of elementary interactions and processes in the world, which can be combined to create models of physical systems. In modern science, the path to serious discovery leads through many steps. We will now analyze the emerging theoretical framework for machine discovery, reconstructing the discovery method at the level of the main goals. These goals and plans that carry them out can be called recursively, until plans are reached which can be directly carried out. Long experience of machine discovery leads to a vision of automated discoverers that consist of several basic building blocks: (1) empirical semantics, (2) experimentation strategies, (3) theory formation from data, (4) recognition of the unknown, (5) identification of similar patterns, that may lead to successful generalizations, (6) theory decomposition to capture elementary physical interactions. Induction, which has been often considered the key element of discovery, is a part of (3), as one of many skills needed in the process. Empirical inquiry requires physical systems to experiment with. In machine discovery little has been done to understand the design of such systems. Rajamoney (1993) considered situations in which two competing theories T1 and T2 cannot be distinguished by experiments on a particular physical set-up S. His system uses S to design other set-ups that permit crucial experiments to distinguish between T1 and T2. Other empirical discovery systems, however, take a set-up experiment S as a given, and only manipulate parameter values within S. 3.1 Empirical Semantics Empirical discoverers use manipulators and sensors to undo the actual experiments. Examples of manipulators are hand, gripper, burrette, or heater. Examples of sensors are eye, camera, balance, or thermometer. Manipulators and sensors are applied to the set-up experiment S, creating states of S desired by the scientist and recording the actual outcomes. Software necessary for real-world experiments includes programs which control sensors and manipulators, so-called device drivers. But meaningful to the discovery process are not individual operations of device drivers, but their combinations, prescribed by operational definitions (Bridgman, 1927; Carnap, 1936; Zytkow, 1982). Operational definitions are algorithms expressed in terms of elementary actions of sensors and manipulators, by which states of S are set or measured. To be scientifically useful, each operational definition must be adjusted to the details of a particular empirical set-up (Zytkow, Zhu & Zembowicz, 1992). Operational definitions, device drivers, concrete devices, and the experiment set-up, form jointly an empirical interpretation of the discovery mechanism. Such an interpretation is needed for a concrete real-world application of an automated discoverer. Discovery by experiments with a simulation requires a similar, albeit much simpler, interface (Shen, 1993). 3.2 Empirical Theory Formation After devices are linked to an empirical system S, and operational procedures are fine-tuned to fit S, we can abstract from empirical semantics, and represent S by a multi-dimensional space E, defined as a Cartesian product of possible values of all parameters that can be controlled or measured in S. Experiments are the only way for obtaining information about E, through data which they generate. Data are generalized into knowledge. The discovery task is to generate as complete and adequate a theory of E as possible, including regularities between control variables and dependent variables, and boundary conditions for regularities. The theory should be adequate within empirical error. The task can also include detection of patterns, such as maxima and discontinuities of dependent variables, and regularities for parameters of those patterns. 3.3 Experimentation Strategies An autonomous explorer controls the values of all independent variables in E and can measure the physical response in terms of values of dependent variables. Each experiment consists of selecting a value for each independent variable, and in measuring the values of all dependent variables. Experiments are typically organized in sequences, to enable better calibration, verification, detection of outliers, and error analysis. The sequences are generated according to different schemas for different goals, such as induction of empirical equations, verification, or detection of the scope of applications of a given theory (Langley et.al. 1987; Kulkarni & Simon, 1987; Koehn & Zytkow, 1986). Shen (1993) considers another experimentation strategy, driven by the need for pieces of knowledge required in problem solving. 3.4 Theory Formation Mechanism Several subgoals may be needed on the way to a complete empirical theory of a multi-dimensional space E. They include discovery of regularities and other patterns in two variables, recursive generalization to further dimensions, discovery of regularity boundaries, data partitioning, identification of similar patterns, and recognition of areas in which theory is still missing. We will now discuss these tasks. Finding the regularities between one control variable and one dependent variable is an important scientific goal, and a subgoal to many others. Such regularities, typically empirical equations, have been the target of discovery systems developed by Gerwin (1974), Langley et al. (1987), Falkenhainer & Michalski (1986), Nordhausen & Langley (1990a), Kokar (1986), Wu & Wang (1989), Wong (1991), Zembowicz & Zytkow (1992), Moulet (1992,1992a), Schaffer (1993), Dzeroski & Todorovski (1993), Cheng & Simon (1992), and others. Quantitative discovery systems traditionally focused on regularities in the form of equations, whereas scientists are often interested in other patterns, such as maxima, minima, and discontinuities. The maxima can, for instance, indicate various chemical species, whereby maximum location indicates the type of ion, while the maximum height indicates the concentration (Zytkow, Zhu, & Hussam, 1990). Discontinuities may indicate phase changes. When an equation Q has been found for a sequence of data, new goals are to find the limits of Q's application and to generalize Q to other control variables. When the former goal is successful, that is, when the boundaries for application of Q have been found, this leads to the goals of finding regularities beyond the boundaries. Generalization, in turn, can be done by recursively invoking the goals of data collection and equation fitting (Langley et al, 1987; Nordhausen & Langley, 1990, 1993; Koehn & Zytkow, 1986). If an equation which would fit the data cannot be found, the data can be decomposed into fragments and the equation finding goal can be invoked for each fragment separately. Data partitioning can use maxima, minima, discontinuities, and other special points detected in the data (Falkenhainer & Michalski, 1986; Rao & Lu, 1992; Zytkow et al. 1990, 1992). If no regularity can be found, a data set can be treated as a regularity in the form of a lookup table used for interpolation. 3.5 Identification of Similar Patterns When many patterns have been detected, it is important to group them together by similarity in meaning. Grouping the corresponding patterns, technically based on their similarity, is a precondition for successful generalization within each group (Zytkow, Zhu & Hussam, 1990). 3.6 Recognition of the Unknown Discoverers explore the unknown. They must be able to examine the existing state of knowledge to find the boundaries that separate the known from the unknown. Then they cross the boundaries to explore the unknown world beyond them. Machine discoverers can use the same strategy (Scott & Markovitch, 1993; Shen, 1993; Zytkow & Zhu, 1993). Each discovery goal corresponds to a limitation of knowledge, for instance, to an area in E which has not been explored, a boundary which has not been determined, and a generalization which have not been made. Not every knowledge representation mechanism makes it easy to determine the unknown. Increasingly, discovery systems use graphs to represent relationships between the incrementally discovered pieces of knowledge, and use frame-like structures to represent knowledge contained in individual nodes in the graphs (Scott & Markowitch, 1993; Nordhausen & Langley, 1990, 1993). A knowledge graph can model the topology of laws and their boundaries in the space E (Zytkow & Zhu, 1991, 1993). The graph which represents the current state of knowledge can be examined at any time to find its limitations, which become new goals for future discovery. We can call this approach knowledge-driven goal generation. Each knowledge state can be transcended in different directions, leading to alternative goals, from which one must be selected according to a selection mechanism. A big advantage of this approach lies in separating knowledge, goals, and discovery methods from each other. The mechanisms for goal generation, selection of the next goal, and selection of the method to approach the goal, can be independent. Other discoverers, using the same knowledge graph, can select different goals and apply different methods. This creates a situation similar to real science, making machine discoverers more flexible and efficient. 3.7 Discovery of Elementary Interactions Thus far we have concentrated on finding a network of empirical equations to describe the space E of many empirical variables over a fixed physical system S. The next important goal leads from the equations to their components that describe elementary interactions in S. Scientists interpret the equations to assign their component terms physical meaning, for instance the momentum or kinetic energy of each individual object in S. Equation transformations leading to such interpretations form a search space explored by Zytkow (1990). 4. Summary Cognitive autonomy of a discoverer is a matter of degree and it grows by acquiring more means, goals, and values. We argued that discovery plays a central role in learning, and that many years of research on discovery systems have identified a small number of generic goals needed to discover empirical theories. The system of goals, methods for goal satisfaction, measuring and manipulating devices, and a network of the discovered knowledge elements, are the basic building blocks with which to construct automated discoverers. Acknowledgement: comments from Peter Edwards helped to clarify many issues. References % Bridgman, P.W. 1927. The Logic of Modern Physics. % Carnap, R. 1936. Testability and Meaning, Philosophy of Science, Vol.3. % Cheng, P.C. & Simon, H.A. 1992. The Right Representation for Discovery: Finding the Conservation of Momentum. in: Sleeman & Edwards eds. Proc. of Ninth Intern. Conference on Machine Learning, 62-71. % Dzeroski, S. & Todorovski, L. 1993. Discovering Dynamics, Proc. of 10th International Conference on Machine Learning, 97-103 % Edwards, P. ed. 1993. Working Notes MLnet Workshop on Machine Discovery. Blanes, Spain, Sep.23. % Falkenhainer, B.C. & Michalski, R.S. 1986. Integrating quantitative and qualitative discovery: The ABACUS system. Machine Learning, Vol.1, 367-401. % Fischer, P., & Zytkow, J.M. 1992. Incremental Generation and Exploration of Hidden Structure, in: Zytkow J. ed. Proc. of ML-92 Workshop on Machine Discovery, Aberdeen, UK, July 4, 103-110. % Gerwin, D.G. 1974. Information processing, data inferences, and scientific generalization, Behav.Sci. 19, 314-325. % Gordon, A. 1992. Informal Qualitative Models in Scientific Discovery. in: Zytkow J. ed. Proc. of ML-92 Workshop on Machine Discovery, Aberdeen, UK, July 4, 98-102. % Karp, P. 1990. Hypothesis Formation as Design. in: J.Shrager & P. Langley eds. Computational Models of Scientific Discovery and Theory Formation, Morgan Kaufmann Publishers, San Mateo, CA, 275-317. % Kocabas, S. 1991. Conflict Resolution as Discovery in Particle Physics. Machine Learning 6, 277-309. % Koehn, B. & Zytkow, J.M. 1986. Experimenting and Theorizing in Theory Formation. in: Ras Z. & Zemankova M. eds. Proc. of the International Symposium on Methodologies for Intelligent Systems. ACM SIGART Press, 296-307. % Kokar, M.M. 1986. Determining Arguments of Invariant Functional Descriptions, Machine Learning, 1, 403-422. % Kulkarni, D., & Simon, H.A. 1987. The Processes of Scientific Discovery: The Strategy of Experimentation, Cognitive Science, 12, 139-175. % Langley, P., Simon, H.A., Bradshaw, G.L. & Zytkow, J.M. 1987. Scientific Discovery: Computational Explorations of the Creative Processes. Cambridge, MA: The MIT Press. % Metaxas, S. 1993. The Prediction of Physical Properties with CRITON. In: Edwards P. ed. Working Notes, MLnet Workshop on Machine Discovery, Blanes, Spain Sep.23, 61-65. % Moulet, M. 1992. A symbolic algorithm for computing coefficients' accuracy in regression, in: Sleeman D. & Edwards P. eds. Proc. of Ninth Intern. Conference on Machine Learning. % Moulet, M. 1992a. ARC.2: Linear Regression In ABACUS, in: Zytkow J. ed. Proc. of ML-92 Workshop on Machine Discovery, Aberdeen, UK, July 4, 137-146. % Nordhausen, B., & Langley, P. 1990. An Integrated Approach to Empirical Discovery. in: J.Shrager & P. Langley eds. Computational Models of Scientific Discovery and Theory Formation. Morgan Kaufmann Publishers, San Mateo, CA. 97-128. % Nordhausen, B., & Langley, P. 1990a. A Robust Approach to Numeric Discovery, Proc. of Seventh International Conference on Machine Learning, Palo Alto, CA: Morgan Kaufmann. 411-418. % Nordhausen, B. & Langley, P. 1993. An Integrated Framework for Empirical Discovery, Machine Learning, 12, 17-47. % Piatetsky-Shapiro, G. ed. 1991. Proc. of AAAI-93 Workshop on Knowledge Discovery in Databases. % Piatetsky-Shapiro, G. ed. 1993. Proc. of AAAI-93 Workshop on Knowledge Discovery in Databases. % Piatetsky-Shapiro, G. & Frawley, W. eds. 1991. Knowledge Discovery in Database}, The AAAI Press, Menlo Park, CA. % Rajamoney, S.A. 1993. The Design of Discrimination Experiments. Machine Learning, 12, 185-203. % Rao, R.B. & Lu S.C. 1992. Learning Engineering Models with the Minimum Description Length Principle, Proc. of Tenth National Conference on Artificial Intelligence, 717-722. % Ras, Z. ed. 1993. Journal for Intelligent Information Systems, Vol.2. % Rose, D. 1989. Using Domain Knowledge to Aid Scientific Theory Revision. Proc. of Sixth Intern. Workshop on Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA. % Rose, D. & Langley, P. 1986. Chemical Discovery as Belief Revision, Machine Learning, 1, 423-451. % Roverso, D., Edwards, P. & Sleeman, D. 1992. Machine Discovery by Model Driven Analogy. in: Zytkow J. ed. Proc. of ML-92 Workshop on Machine Discovery, Aberdeen, UK, July 4, 87-97. *********************************** Mobal 3.0 Released The ML group at GMD have now released Mobal 3.0, an enhanced version of their knowledge acquisition and machine learning system for first- order KBS development on Sparc workstations. Mobal is a multistrategy learning system that integrates a manual knowledge acquisition and inspection environment, a powerful first-order inference engine, and various machine learning methods for automated knowledge acquisition, structuring, and theory revision. As the most visible change, the new release 3.0 no longer requires Open Windows, but features an X11 graphical user interface built using Tcl/Tk. This should make installation trouble-free for most users, and through its networked client-server structure, allows easy integration with other programs. As a second change resulting from work in the ILP ESPRIT Basic Research project, Mobal 3.0 now offers an "external tool" facility that allows other (ILP) learning algorithms to be interfaced to the system and used from within the same knowledge acquisition environment. The current release of Mobal includes interfaces to GOLEM by S. Muggleton and C. Feng (Oxford University), GRDT by V. Klingspor (Univ. Dortmund) and FOIL 6.1 by R. Quinlan and M. Cameron- Jones (Sydney Univ.). GMD grants a cost-free license to use Mobal for academic purposes. The system can be obtained from ftp.gmd.de, directory /ml- archive/GMD/software/Mobal (login anonymous, password your E-Mail address). For details about the scientific background of Mobal, see the book "Knowledge Acquisition and Machine Learning", by K. Morik, S. Wrobel, J.-U. Kietz and W. Emde (Academic Press, 1993). A user guide is available via FTP. *********************************** News from the University of Dortumnd Katharina Morik's move from GMD to the University of Dortmund as a full professor in AI is now complete. Following her appointment to Dortmund in 1991, she had continued as external representative of GMD's MLT project, with Stefan Wrobel taking care of internal business. With the end of the MLT project in mid-1993, the ML group at GMD is now lead by Stefan Wrobel. The group consist of four scientists (Werner Emde, Jrg-Uwe Kietz, Edgar Sommer and Stefan Wrobel) and two students (Roman Englert and Marcus Lbbe), and will further develop Mobal and other ML systems. As in the past, the groups at University of Dortmund and at GMD will continue to collaborate closely. *********************************** 1996 INTERNATIONAL MACHINE LEARNING CONFERENCE CALL FOR PROPOSALS In 1993 the ML Journal Board agreed that a cycle of meetings would be set up so that this conference will be held on the Eastern Coast of America, West Coast of America and Europe on a three yearly cycle. In June (or July) 1996, the Thirteenth International Machine Learning Conference will be held at a European site. The purpose of this call is to invite groups interested in organizing and hosting the conference to submit proposals. The group selected to run the conference will be given full authority and responsibility for organising the conference. Proposals should address the following issues: 1. Organization and Format. In previous years, the format of the conference has alternated annually between a single plenary session (1988, 1990) and a set of parallel workshop sessions (1989, 1991). However, since 1992, the format has involved 3 days of plenary sessions preceded or followed by one day of specialized workshops. In 1992 a poster session was held, while in 1993 and 1994, parallel sessions were held in addition to the plenary session. Please indicate which format you propose and how you would arrange the schedule to suit the format. You may present more than one possible format, if you wish. In the past, the conference has been organized by an Organizing Committee whose membership included organizers of past conferences and other senior researchers. Review of papers has been conducted by a Program Committee selected by the organizers with the advice of the Organizing Committee. Most recently, all reviewing has been conducted via email. Please indicate what organization you would employ. 2. Local Parameters. % Accessibility. Is it easy and inexpensive for people (especially graduate students) to travel to the conference site? (Compute mean airfares from Europe and North America.) % Meeting Rooms, AV Equipment, etc. What are the physical facilities like? % Meals and Lodging. Is there low-cost, medium quality housing available for attendees (especially graduate students)? How far is this housing from the meeting rooms? How will attendees get between the two sites? Where will attendees eat? % Demo facilities. Will there be computing equipment and space available to support demos? 3. Local Machine Learning Community. Is there a local ML group/community that can help with organization and funding? 4. Organizational and Financial Support. Can the host institution(s) provide support for registration and financial management (e.g., credit card payments, accounting, etc.). How will the conference be funded? Provide a draft budget covering expenses, expected registration fee schedule, and sources of financial support (this is very important). The host institution must agree to forward any unused funds to the host of the 1997 conference. In previous years, funding has been obtained from federal granting agencies, corporations, and universities. Proposals should be sent before July 5, 1994 to Tom Diettrich Department of Computer Science 303 Dearborn Hall Oregon State University Corvallis, OR 97331-3202 tgd@cs.orst.edu fax: 503-737-3014 Email is preferred. The choice of organizers will be made at the July meeting of the Editorial Board of the Machine Learning Journal, which is tentatively scheduled for the evening of July 11 during the 1994 International Machine Learning Conference at Rutgers University. *********************************** Technical Meetings Between MLnet and Other Networks A joint meeting with ELSNET* is planned (details will be announced shortly). Proposals are welcomed from Network members for joint meetings between MLnet and other NoEs. Generally these meetings will receive some financial support from MLnet. Please send proposals to: Derek Sleeman Fax: + 44 224 273422 email: sleeman@csd.abdn.ac.uk Alternatively, he would also be happy to discuss outline ideas for such meetings. You are encouraged to start discussing details well before the planned dates. *********************************** News from our "data base" of industrial applications We gathered information from almost 60 European companies involved in Machine Learning or Knowledge Acquisition. This data has been sent back to the concerned companies in order to get their approval for publication, (especially for those that provided us with information that has been tailored to our format). We have since started investigating as well applications in domains near to ours: applications of Neural Networks, Genetic Algorithms, and Case-Based Reasoning. If readers know of any company involved in these topics, please let us know. Information to: Dr Yves Kodratoff CNRS & Universite Paris-Sud LRI, Batiment 490 91405 Orsay France email: yk@lri.lri.fr Fax: +33 1 6941 6586 *********************************** Procedures for joining MLnet Initial enquiries will receive a standard information pack (including a copy of the Technical Annex) All centres interested in joining MLnet are asked to send the following to MLnet's Academic Coordinator: % A signed statement on Institutional notepaper saying that you have read and agreed with the general aims of MLnet given in the Technical Annex; % One hard-copy document listing the Machine Learning (and related activities) at the proposed node and three copies of any enclosures; the document should include a list of scientists involved in these field(s), half page curriculum vitae for each of these senior scientists, current research students, lists of recent grants and relevant publications over the last 5 year period; % A statement of the Technical Committees which the Centre would be interested in joining, and a succinct statement of the potential contributions of the Centre to the Network and its Technical Committees. Two members of the Management Board will be asked to look at the material in detail and will present the proposal at the next Management Board meeting. Through the Network's Coordinator, the members may ask for additional information. The Academic Coordinator will be in touch with the Centre as soon as possible after the Management Board meeting. The Management Board is not planning to set a fixed timetable for applications, but advises potential nodes that it currently holds Management Board meetings in November, April and September, and that papers would have to be received at least six weeks before a Management Board meeting to be considered. (Contact Derek Sleeman for details). *********************************** Main Nodes Professor D Sleeman, Aberdeen University (GB) Tel No: +44 224 27 2288/2304 Fax No: +44 224 27 3422 Professor B J Wielinga, University of Amsterdam (NL) Tel No: +31 20 525 6789/6796 Fax No: +31 20 525 6896 Professor R Lopez de Mantaras, IIIA AIRI, Blanes (ES) Tel No: +34 72 336 101 Fax No: +34 72 337 806 Professor K Morik, Dortmund University (DE) Tel No: +49 231 755 5101 Fax No: +49 231 755 5105/2047 Dr L DeRaedt, Leuven Katholieke Universiteit (BE) Tel No: +32 16 20 10 15 Fax No: +32 16 20 53 08 Dr Y Kodratoff, Paris Sud University, Orsay (FR) Tel No: +33 1 69 41 69 04 Fax No: +33 1 69 41 65 86 Professor L Saitta, Torino University (IT) Tel No: +39 11 742 9214/5 Fax no: +39 11 751 603 Dr Gholamreza Nakhaeizadeh, Daimler-Benz, Ulm (DE) Tel No: +49 731 505 2860 Fax No: +49 731 505 4210 Mr T Parsons, British Aerospace plc, Bristol (GB) Tel No: +44 272 363 458 Fax no: +44 272 363 733 Mr F Malabocchia, CSELT S.p.A., Torino, (IT) Tel No: +39 11 228 6778 Fax No: +39 11 228 5520 Dr D Cornwell, CEC Project Officer Tel No: +32 2 296 8664/8071 Fax No: +32 2 296 8390/8397 Associate Nodes % Alcatel Alsthom Recherche, Marcoussis (FR) % ARIAI, Vienna (AT) % Bari University (IT) % Bradford University (GB) % Catania University (IT) % Coimbra University (PT) % CRIM-ERA, Montpellier (FR) % Electricite de France, Clamart , Paris (FR) % FORTH, Crete (GR) % Frankfurt University (DE) % GMD, Bonn (DE) % Kaiserslautern University (DE) % Karlsruhe University (DE) % Ljubljana AI Labs (SL) %JNottingham University (GB) % Oporto University (PT) % Oxford University (UK) % Paris VI University (FR) % Pavia University (IT) % Prague University (CZ) % Reading University (GB) % Savoie University, Chambery (FR) % Stockholm University (SE) % Tilburg University (NL) % Trinity College, Dublin (IE) % Ugo Bordoni Foundation, Roma (IT) % VUB, Brussels (BE) % ISoft, Gif sur Yvette (FR) %JMatra Marconi Space, Toulouse (FR) % Siemens AG, Munich (DE) Academic Coordinator: Derek Sleeman Department of Computing Science University of Aberdeen King's College Aberdeen AB9 2UE Scotland, UK Tel: +44 224 27 2288/2304 Fax: +44 224 27 3422 email: {mlnet, sleeman}@csd.abdn.ac.uk Documents available from Aberdeen: State of the Art Overview of ML and KA Recently Announced projects (ESPRIT III) MLnet Flyer First Year Report Policy Statement ********************************** MLnet NEWS 2.3 END ********************************** ------------------------------ End of ML-LIST (Digest format) ****************************************