Received: from ics.uci.edu by Paris.ics.uci.edu id aa01096; 20 Dec 92 15:09 PST Received: from ics.uci.edu by q2.ics.uci.edu id aa03234; 20 Dec 92 15:09 PST To: ML-LIST: ; Subject: Machine Learning List: Vol. 4 No. 24 Reply-to: ml@ics.uci.edu Date: Sun, 20 Dec 92 14:56:37 -0800 From: Michael Pazzani Message-ID: <9212201509.aa03234@q2.ics.uci.edu> Machine Learning List: Vol. 4 No. 24 Sunday, Dec 20, 1992 Contents: IJCAI-93 Workshop on Inductive Logic Programming CONNECTIONIST MODELS SUMMER SCHOOL Senior Cognitive Science Faculty Position call for papers "AI and Genome" Call For Papers: ACM TIS Special Issue on Text Categorization Morgan Kaufmann book announcement 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: Administrative Note: I'm leaving on a sabbatical until April 1. I will still moderate ML-LIST, but you can help by: 1. Sending submissions to ml@ics.uci.edu (rather than pazzani@ics.uci.edu) 2. Send submissions that look like other items in ML-LIST a. Use a descriptive subject (Not "Please post") b. Send text, not Tex or troff c. Don't use dashes "---" to separate parts of your message Happy Holidays- Mike ---------------------------------------------------------------------- Date: Fri, 18 Dec 92 13:33:04 +0100 From: " F. Bergadano" Subject: IJCAI-93 Workshop on Inductive Logic Programming IJCAI-93 Workshop on Inductive Logic Programming Call for Papers Inductive Logic Programming is mainly concerned with the inductive synthesis of logic programs. As such, it is closely related to Machine Learning and Logic Programming, and has evolved from these areas to a growing field of research for both Artificial Intelligence and Software Engineering. For Machine Learning, the problem is a natural extension of previous methods of inductive generalization to the case of logic-based and recursive concept descriptions. For Logic Programming, inductive methods provide the user with a tool for programming not only with clauses, but also with examples and general constraints. The workshop intends to address both aspects of ILP, and provide a common ground for discussion and for the presentation of algorithms and results. Attendance will be limited to 50 participants, on the basis of submitted papers and participation requests sent to any of the program chairs. Participants will be required to have registered at IJCAI. Program Co-chairs: Francesco Bergadano Luc De Raedt Dipartimento di Matematica Departement Computerwetenschappen Universita` di Catania Kathol. University of Leuven Via Andrea Doria 6/a Celestijnenlaan 200a Catania, Italy B-3001 Leuven, Belgium tel (+39) 95 330533 tel (+32) 16200656 fax (+39) 95 330094 fax (+32) 16205308 bergadan@mathct.cineca.it lucdr@cs.kuleuven.ac.be Stan Matwin Stephen Muggleton Department of Computer Science Oxford University Computing Lab University of Ottawa 11 Keble Road Ottawa, Ontario KIN9B4, Oxford, OX1 3QD, UK CANADA tel (+44) 865 272562 tel (+1) 613 5645069 fax (+44) 865 272582 stan@csi.uottawa.ca steve@prg.oxford.ac.uk Papers (a maximum of 10 double spaced pages) should be submitted in 2 copies to any of the above program co-chairs, with the following deadlines: submitted paper must be received before: March 25th, 1993 notification of acceptance/rejection: May 5th, 1993 final camera-ready paper: June 10th, 1993 workshop: 28th of August, in Chambery just before IJCAI-93 ------------------------------ Date: Mon, 7 Dec 1992 22:22:05 -0700 From: "Michael C. Mozer" Subject: CONNECTIONIST MODELS SUMMER SCHOOL CALL FOR APPLICATIONS CONNECTIONIST MODELS SUMMER SCHOOL University of Colorado Boulder, Colorado June 21 - July 3, 1993 The University of Colorado will host the 1993 Connectionist Models Summer School from June 21 to July 3, 1993. The purpose of the summer school is to provide training to promising young researchers in connectionism (neural networks) by leaders of the field and to foster interdisciplinary collaboration. This will be the fourth such program in a series that was held at Carnegie-Mellon in 1986 and 1988 and at UC San Diego in 1990. Previous summer schools have been extremely successful and we look forward to the 1993 session with anticipation of another exciting event. The summer school will offer courses in many areas of connectionist modeling, with emphasis on artificial intelligence, cognitive neuroscience, cognitive science, computational methods, and theoretical foundations. Visiting faculty (see list of invited faculty below) will present daily lectures and tutorials, coordinate informal workshops, and lead small discussion groups. The summer school schedule is designed to allow for significant interaction among students and faculty. As in previous years, a proceedings of the summer school will be published. Applications will be considered only from graduate students currently enrolled in Ph.D. programs. About 50 students will be accepted. Admission is on a competitive basis. Tuition will be covered for all students, and we expect to have scholarships available to subsidize housing and meal costs, which will run approximately $300. Applications should include the following materials: * a one-page statement of purpose, explaining major areas of interest and prior background in connectionist modeling and neural networks; * a vita, including academic history, list of publications (if any), and relevant courses taken with instructors' names and grades received; * two letters of recommendation from individuals familiar with the applicants' work; and * if room and board support is requested, a statement from the applicant describing potential sources of financial support available (department, advisor, etc.) and the estimated extent of need. We hope to have sufficient scholarship funds available to provide room and board to all accepted students regardless of financial need. Applications should be sent to: Connectionist Models Summer School c/o Institute of Cognitive Science Campus Box 344 University of Colorado Boulder, CO 80309 All application materials must be received by March 1, 1993. Decisions about acceptance and scholarship awards will be announced April 15. If you have additional questions, please write to the address above or send e-mail to "cmss@cs.colorado.edu". Organizing Committee Jeff Elman (UC San Diego) Mike Mozer (University of Colorado) Paul Smolensky (University of Colorado) Dave Touretzky (Carnegie-Mellon) Andreas Weigend (Xerox PARC and University of Colorado) Additional faculty will include: Andy Barto (University of Massachusetts, Amherst) Jack Cowan (University of Chicago) David Haussler (UC Santa Cruz) Geoff Hinton (University of Toronto) Mike Jordan (MIT) John Kruschke (Indiana University) Jay McClelland (Carnegie-Mellon) Ennio Mingolla (Boston University) Steve Nowlan (Salk Institute) Dave Plaut (Carnegie-Mellon) Jordan Pollack (Ohio State) Dave Rumelhart (Stanford) Terry Sejnowski (UC San Diego and Salk Institute) The Summer School is sponsored in part by the American Association for Artificial Intelligence, the International Neural Network Society, and Siemens Research Center. ------------------------------ Date: Thu, 10 Dec 92 13:34:54 EST From: Tony Simon Subject: Senior Cognitive Science Faculty Position SENIOR FACULTY POSITION IN COGNITIVE PSYCHOLOGY GEORGIA INSTITUTE OF TECHNOLOGY COGNITIVE SCIENCE - The School of Psychology at the Georgia Institute of Technology is searching for a senior faculty member in Cognitive Psychology to be part of a major interdisciplinary thrust in Cognitive Science. Cognitive Science at Georgia Tech includes basic and applied research in focus areas including: (1) learning; (2) problem solving; (3) language and communication; and (4) design. Candidates for this position should add strength and intellectual leadership to one or more of these areas. Assuming resources are available, the appointment could begin in the fall of 1993. Qualifications should include evidence of outstanding research achievement and a commitment to working with Cognitive Scientists from other disciplines. Responsibilities will include intellectual leadership, maintenance of strong programmatic research, supervision of graduate student research, and classroom instruction. To apply, send vitae and names of references to: Cognitive Psychology Search Committee School of Psychology Georgia Institute of Technology Atlanta, GA 30332-0170 Georgia Tech is an Equal Opportunity/Affirmative Action Employer and a member institution of the University System of Georgia. ------------------------------ Date: Thu, 17 Dec 92 19:24:58 +0100 From: "irina Tchoumatchenko 46.42.32.00 poste 433" Subject: call for papers "AI and Genome" WORKSHOP "ARTIFICIAL INTELLIGENCE and the GENOME" at the International Joint Conference on Artificial Intelligence IJCAI-93 August 29 - September 3, 1993 Chambery, FRANCE There is a great deal of intellectual excitement in molecular biology (MB) right now. There has been an explosion of new knowledge due to the advent of the Human Genome Program. Traditional methods of computational molecular biology can hardly cope with important complexity issues without adapting a heuristic approach. They enable one to explicitate molecular biology knowledge to solve a problem as well as to present the obtained solution in biologically-meaningful terms. The computational size of many important biological problems overwhelms even the fastest hardware by many orders of magnitude. The approximate and heuristic methods of Artificial Intelligence have already made significant progress in these difficult problems. Perhaps one reason is great deal of biological knowledge is symbolic and complex in their organization. Another reason is the good match between biology and machine learning. Increasing amout of biological data and a significant lack of theoretical understanding suggest the use of generalization techniques to discover "similarities" in data and to develop some pieces of theory. On the other hand, molecular biology is a challenging real-world domain for artificial intelligence research, being neither trivial nor equivalent to solving the general problem of intelligence. This workshop is dedicated to support the young AI/MB field of research. TOPICS OF INTEREST INCLUDE (BUT ARE NOT RESTRICTED TO): ******************************************************* *** Knowledge-based approaches to molecular biology problem solving; Molecular biology knowledge-representation issues, knowledge-based heuristics to guide molecular biology data processing, explanation of MB data processing results in terms of relevant MB knowledge; *** Data/Knowledge bases for molecular biology; Acquisition of molecular biology knowledge, building public genomic knowledge bases, a concept of "different view points" in the MB data processing context; *** Generalization techniques applied to molecular biology problem solving; Machine learning techniques as well as neural network techniques, supervised learning versus non-supervised learning, scaling properties of different generalization techniques applied to MB problems; *** Biological sequence analysis; AI-based methods for sequence alignment, motif finding, etc., knowledge-guided alignment, comparison of AI-based methods for sequence analysis with the methods of computational biology; *** Prediction of DNA protein coding regions and regulatory sites using AI-methods; Machine learning techniques, neural networks, grammar-based approaches, etc.; *** Predicting protein folding using AI-methods; Predicting secondary, super-secondary, tertiary protein structure, construction protein folding prediction theories by examples; *** Predicting gene/protein functions using AI-methods; Complexity of the function prediction problem, understanding the structure/function relationship in biologically-meaningful examples, structure/functions patterns, attempts toward description of functional space; *** Similarity and homology; Similarity measures for gene/protein class construction, knowledge-based similarity measures, similarity versus homology, inferring evolutionary trees; *** Other perspective approaches to classify and predict properties of MB sequences; Information-theoretic approach, standard non-parametric statistical analysis, Hidden Markov models and statistical physics methods; INVITED TALKS: ************** L. Hunter, NLM, AI problems in finding genetic sequence motifs J. Shavlik, U. of Wisconsin, Learning important relations in protein structures B. Buchanan, U. of Pittsburgh, to be determined R. Lathrop, MIT, to be determined Y. Kodratoff, U. Paris-Sud, to be determined J.-G. Ganascia, U. Paris-VI, Application of machine learning techniques to the biological investigation viewed as a constructive process SCHEDULE ********** Papers received: March 1, 1993 Acceptance notification: April 1, 1993 Final papers: June 1, 1993 WORKSHOP FORMAT: ****************** The format of the workshop will be paper sessions with discussion at the end of each session, and a concluding panel. Prospective particitants should submit papers of five to ten pages in length. Four paper copies are required. Those who would like to attend without a presentation should send a one to two-page description of their relevant research interests. Attendance at the workshop will be limited to 30 or 40 people. Each workshop attendee MUST HAVE REGISTERED FOR THE MAIN CONFERENCE. An additional (low) 300 FF fee for the workshop attendance (about $60) will be required. One student attending the workshop normally (has registered for the main conference) and being in charge of taking notes during the entirre workshop, could be exempted from the additional 300 FF fee. Volunteers are invited. ORGANIZING COMMITTEE ******************** Buchanan, B. (Univ. of Pittsburgh - USA) Ganascia, J.-G., chairperson (Univ. of Paris-VI - France) Hunter, L. (National Labrary of Medicine - USA) Lathrop, R. (MIT - USA) Kodratoff, Y. (Univ. of Paris-Sud - France) Shavlik, J. W. (Univ. of Wisconsin - USA) PLEASE, SEND SUBMISSIONS TO: *************************** Ganascia, J.-G. LAFORIA-CNRS University Paris-VI 4 Place Jussieu 75252 PARIS Cedex 05 France Phone: (33-1)-44-27-47-23 Fax: (33-1)-44-27-70-00 E-mail: ganascia@laforia.ibp.fr ------------------------------ Date: Sun, 13 Dec 92 16:55 EST From: David Lewis Subject: Call For Papers: ACM TIS Special Issue on Text Categorization This CFP may be of interest to readers. Learning techniques have been widely used in producing text categorization systems. A range of data sets and tasks with interesting properties are available: small to large numbers of examples, small to large numbers of categories (disjoint or overlapping), high dimensionality feature sets, available knowledge bases, time-varying data, willing human judges (with varying degrees of consistency), real applications, etc. Dave **************************************************************************** Call For Papers Special Issue on Text Categorization ACM Transactions on Information Systems Submissions due: June 1, 1993 Text categorization is the classification of units of natural language text with respect to a set of pre-existing categories. Reducing an infinite set of possible natural language inputs to a small set of categories is a central strategy in computational systems that process natural language. Some uses of text categorization have been: --To assign subject categories to documents in support of text retrieval and library organization, or to aid the human assignment of such categories. --To route messages, news stories, or other continuous streams of texts to interested recipients. --As a component in natural language processing systems, to filter out nonrelevant texts and parts of texts, to route texts to category-specific processing mechanisms, or to extract limited forms of information. --As an aid in lexical analysis tasks, such as word sense disambiguation. --To categorize nontextual entities by textual annotations, for instance to assign people to occupational categories based on free text responses to survey questions. ACM Transactions on Information Systems is the leading forum for presenting research on text processing systems. For this special issue we encourage the submission of high quality technical descriptions of algorithms and methods for text categorization. Experiments comparing alternative methods are especially welcome, as are results on deploying systems into regular use. Five copies of each manuscript should be submitted to either of the special issue editors at the addresses below: David D. Lewis Philip J. Hayes AT&T Bell Laboratories Carnegie Group, Inc. 600 Mountain Ave. Five PPG Place Room 2C409 Pittsburgh, PA 15222 Murray Hill, NJ 07974 USA USA hayes@cgi.com lewis@research.att.com Submission June 1, 1993 Notification October 1, 1993 Revision February 1, 1994 Publication mid-1994 The July 1990 issue of TIS contains a description of the style requirements. **************************************************************************** David D. Lewis email: lewis@research.att.com AT&T Bell Laboratories ph. 908-582-3976 600 Mountain Ave.; Room 2C409 Murray Hill, NJ 07974; USA ------------------------------ Date: Wed, 9 Dec 92 10:50:17 PST From: Morgan Kaufmann Subject: Morgan Kaufmann book announcement MORGAN KAUFMANN ANNOUNCES THE PUBLICATION OF: READINGS IN KNOWLEDGE ACQUISITION AND LEARNING: Automating the Construction and Improvement of Expert Systems Edited by Bruce G. Buchanan (University of Pittsburgh) and David C. Wilkins (University of Illinois) ISBN 1-55860-163-5 912 pages $44.95 U.S. $49.95 International READINGS IN KNOWLEDGE ACQUISITION AND LEARNING collects the best of the artificial intelligence literature from the fields of machine learning and knowledge acquisition. This book brings together for the first time the perspectives on constructing knowledge-based systems from these two historically separate subfields of artificial intelligence. A key criterion for article inclusion is an empirical demonstration that the method described in the paper successfully automates some important aspect of creating and maintaining a knowledge-based system. In addition to the papers, the editors provide an introduction to the field and to each group of papers, discussing their significance and pointing to related work. This book can serve as a text for courses and seminars in artificial intelligence, expert systems, knowledge acquisition, knowledge engineering, and machine learning. It will also provide practical ideas for professionals engaged in the building and maintenance of knowledge-based systems. Table of Contents Chapter 1 Overview of Knowledge Acquisition and Learning 1 1.1 Overviews 1.1.1 R. S. Michalski 7 Toward a unified theory of learning: Multistrategy task-adaptive learning 1.1.2 J. H. Boose 39 A survey of knowledge acquisition techniques and tools Chapter 2 Expertise and Expert Systems 57 2.1 Expertise and Its Acquisition 59 2.1.1 J. R. Anderson 61 Development of expertise 2.1.2 M. L. G. Shaw and J. B. Woodward 78 Modeling expert knowledge 2.1.3 B. J. Wielinga, A. Th. Schreiber, & J. A. Breuker 92 KADS: A modelling approach to knowledge engineering 2.1.4 D. E. Forsythe and B. G. Buchanan 117 Knowledge acquisition for expert systems: Some pitfalls and suggestions 2.2 Expert Systems and Generic Problem Classes 125 2.2.1 B. G. Buchanan and R. G. Smith 128 Fundamentals of expert systems 2.2.2 J. McDermott 150 Preliminary steps toward a taxonomy of problem-solving methods 2.2.3 B. Chandrasekaran 171 Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design 2.2.4 W. J. Clancey 176 Acquiring, representing, and evaluating a competence model of diagnostic strategy Chapter 3 Interactive Elicitation Tools 217 3.1 Eliciting Classification Knowledge 219 3.1.1 R. Davis 221 Interactive transfer of expertise: Acquisition of new inference rules 3.1.2 J. H. Boose and J. M. Bradshaw 240 Expertise transfer and complex problems: Using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems 3.1.3 L. Eshelman, D. Ehret, J. McDermott, and M. Tan 253 MOLE: A tenacious knowledge-acquisition tool 3.2 Eliciting Design Knowledge 261 3.2.1. S. Marcus and J. McDermott 263 SALT: A knowledge acquisition language for propose-and-revise systems 3.2.2 M. A. Musen 282 Automated support for building and extending expert models 3.2.3 T. R. Gruber 297 Automated knowledge aquisition for strategic knowledge Chapter 4 Inductive Generalization Methods 319 4.1 Learning Classification Knowledge 321 4.1.1 R. S. Michalski 323 A theory and methodology of inductive learning 4.1.2 J. R. Quinlan 349 Induction of decision trees 4.1.3 G. E. Hinton 362 Connectionist learning procedures 4.1.4. A. Ginsberg, S. M. Weiss, and P. Politakis 387 Automatic knowledge base refinement for classification systems 4.2 Learning Classes Via Clustering 403 4.2.1 J. H. Gennari, P. Langley, and D. Fisher 405 Models of incremental concept formation 4.2.2 P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freeman 431 Autoclass: A Bayesian classification system 4.3 Measurement and Evaluation of Learning Systems 443 4.3.1 J. W. Shavlik, R. Mooney, and G. G. Towell 445 Symbolic and neural learning algorithms: An experimental comparison 4.3.2 B. R. Gaines 462 The quantification of knowledge: Formal foundations for knowledge acquisition methodologies 4.3.3 T. G. Dietterich 475 Limitations on inductive learning Chapter 5 Compilation and Deep Models 481 5.1 Compilation of Knowledge for Efficiency 483 5.1.1 R. E. Fikes, P. E. Hart, and N. J. Nilsson 485 Learning and executing generalized robot plans 5.1.2 T. M. Mitchell, P. E. Utgoff, and R. Banerji 504 Learning by experimentation: Acquiring and refining problem-solving heuristics 5.1.3 J. E. Laird, P. S. Rosenbloom, and A. Newell 518 Chunking in SOAR: The anatomy of a general learning mechanism 5.2 Explanation-Based Learning of Classification Knowledge 537 5.2.1 T. M. Mitchell, R. M. Keller, S. T. Kedar-Cabelli 539 Explanation-based generalization: A unifying view 5.2.2 R. J. Mooney 556 Explanation generalization in EGGS 5.3 Synthesizing Problem Solvers from Deep Models 577 5.3.1 W. R. Swartout 579 XPLAIN: A system for creating and explaining expert consulting programs 5.3.2 D. R. Barstow 600 Domain-specific automatic programming 5.3.3 I. Bratko 616 Qualitative modelling and learning in KARDIO Chapter 6 Apprenticeship Learning Systems 627 6.1 Apprentice Systems for Classification Knowledge 629 6.1.1 D. C. Wilkins 631 Knowledge base refinement as improving an incomplete and incorrect domain theory 6.2 Apprentice Systems for Design Knowledge 643 6.2.1 T. M. Mitchell, S. Mabadevan, L. I. Steinberg 645 LEAP: A learning apprentice for VLSI design 6.2.2 Y. Kodratoff and G. Tecuci 655 Techniques of design and DISCIPLE learning apprentice Chapter 7 Analogical and Case-Based Reasoning 669 7.1 Analogical Reasoning 671 7.1.1 D. Gentner 673 The mechanisms of analogical learning 7.1.2 B. Falkenhainer, K. D. Forbus, and D. Gentner 695 The structure-mapping engine: Algorithm and examples 7.1.3 J. G. Carbonell 727 Derivational analogy: A theory of reconstructive problem solving and expertise acquisition 7.2 Case-Based Reasoning 739 7.2.1 B. W. Porter, R. Bareiss, and R. C. Holte 741 Concept learning and heuristic classification in weak-theory domains 7.2.2 A. R. Golding and P. S. Rosenbloom 759 Improving rule-based systems through case-based reasoning 7.2.3 K. J. Hammond 765 Explaining and repairing plans that fail Chapter 8 Discovery and Commonsense Reasoning 793 8.1 Discovery Learning 795 8.1.1 D. B. Lenat 797 The ubiquity of discovery 8.1.2 L. B. Booker, D. E. Goldberg, and J. H. Holland 812 Classifier systems and genetic algorithms 8.2 Commonsense Knowledge 837 8.2.2 R. Guha and D. B. Lenat 839 CYC: A midterm report References 867 OTHER TITLES OF INTEREST FROM MORGAN KAUFMANN: C4.5: Programs for Machine Learning, by J. Ross Quinlan (University of Sydney) Case-Based Reasoning, by Janet Kolodner (Georgia Institute of Technology) Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems, by Sholom M. Weiss and Casimir A. Kulikowski (Rutgers University) Readings in Machine Learning, edited by Jude W. Shavlik (University of Wisconsin) and Thomas G. Dietterich (Oregon State University) Ordering Information: Shipping: In the U.S. and Canada, please add $3.50 for the first book and $2.50 for each additional for surface shipping; for surface shipments to all other areas, please add $6.50 for the first book and $3.50 for each additional book. Air shipment available outside North America for $35.00 on the first book, and $25.00 on each additional book. American Express, Master Card, Visa and personal checks drawn on U.S. banks accepted. MORGAN KAUFMANN PUBLISHERS, INC. Department E17 2929 Campus Drive, Suite 260 San Mateo, CA 94403 USA Phone: (800) 745-7323 (in North America) (415) 578-9911 Fax: (415) 578-0672 email: morgan@unix.sri.com ------------------------------ End of ML-LIST (Digest format) ****************************************