From syali@cs.buffalo.edu Tue Feb 8 23:36:41 EST 1994 Article: 1203 of comp.ai.nat-lang Xref: glinda.oz.cs.cmu.edu comp.ai:20599 comp.ai.nat-lang:1203 Newsgroups: comp.ai,comp.ai.nat-lang Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!godot.cc.duq.edu!toads.pgh.pa.us!news.sei.cmu.edu!cis.ohio-state.edu!magnus.acs.ohio-state.edu!math.ohio-state.edu!howland.reston.ans.net!europa.eng.gtefsd.com!news.umbc.edu!eff!news.kei.com!ub!acsu.buffalo.edu!syali From: syali@cs.buffalo.edu (Sy Ali) Subject: CFP: KR for NLP in Implemented Systems (AAAI Fall '94 Symposium) Message-ID: Sender: nntp@acsu.buffalo.edu Nntp-Posting-Host: hydra.cs.buffalo.edu Organization: State University of New York at Buffalo/Comp Sci Date: Tue, 8 Feb 1994 21:15:13 GMT Lines: 149 AAAI 1994 Fall Symposium Knowledge Representation for Natural Language Processing in Implemented Systems November 4-6, 1994 The Monteleone Hotel, New Orleans, Louisiana Call for Participation This symposium is intended to be a meeting of researchers actively working on implemented knowledge representation and reasoning (KRR) systems for general natural language processing (NLP) in order to assess the current state of that field. Specific topics of interest include the following: Expressiveness and generality of the representation language with respect to natural language. For example, coverage of complex object descriptions and treatment of quantification. What are the trade-offs in increasing the expressiveness of the representation language to support natural language? Inference methods that parallel reasoning in natural language. Natural deduction systems, for example, are so called because of the apparent naturalness of the proof procedure. Another example is surface reasoning, based on the syntactic structure of the natural language. Ability of the formalism or system to capture important semantic and pragmatic aspects of natural language. For example, the computational relationship between the representation language and the parser/generator. Is it possible to define the representation language to facilitate this relationship? At what cost? How many or kinds of representation languages are needed for general NLP? Many NLP systems actually use two representation languages: a semantic representation language that captures the semantics of a sentence, and a knowledge representation language that is used to do reasoning and represent the system's general knowledge about the domain. Typically these languages are quite different, with the former being a much more powerful language (including modalities, lambda expressions and other higher order constructs, generalized quantifiers, etc) and the latter being what the system's reasoning engine actually operates on, usually something more or less equivalent to first-order logic, (or an even more restricted vivid representation like a relational database) This raises the question of how these languages relate to each other, and if it is possible (or desirable) to have a semantic representation language that supports inference. Stands on issues such as: - What problems are solved, and how to use the solution(s). - What areas need work. - Defense or attacks of the standard design of morphology-syntax- semantics-pragmatics. Submissions to the symposium should address these topics by showing some text that an actual implemented system can understand, how the information contained in that text is represented, what background information is used by the system, how that information is represented, how the system processes the knowledge to do interesting things (such as answering interesting questions about the text), and how the information is processed into answers. Reports on projects whose purpose is to simulate human understanding of texts will be preferred over projects whose purpose is to provide natural language interfaces to databases, planners, or to pragmatically oriented knowledge bases. The format of this symposium will be designed to encourage interaction amongst the participants. To this end, new, previously unpublished work- in-progress on the topics of the symposium is most desirable, as are stands on the issues outlined above. Submission Information Potential attendees should submit an extended abstract of no more than 10 pages (exclusive of references), twelve point, double-spaced, with one inch margins. Submissions not conforming to these guidelines will not be reviewed. The symposium format will also include one or more panel discussions on the issues listed above. Attendees wishing to participate in panels, or wishing to suggest other panel topics, should indicate their interest and include a current vita along with their extended abstract. Demonstrations of working NLP/KRR systems are also of interest, however attendees must provide their own hardware and software support. Attendees interested in this option should indicate what they are planning on demonstrating, and how they propose to do so. This information should be provided with their extended abstract in a cover letter that clearly states their interest in this option. Email submission is preferred, and should be directed to the Symposium Chair at ssa231f@csm560.smsu.edu and syali@cs.buffalo.edu Preferred email submission formats are: stand-alone LaTeX, PostScript, or plain text (for abstracts without complex figures, etc). If email submission is not possible, then five copies of the paper should be mailed to the Symposium Chair: Syed S. Ali Chair, AAAI Fall Symposium on Knowledge Representation for Natural Language Processing in Implemented Systems Department of Computer Science Southwest Missouri State University 901 South National Avenue Springfield, MO 65804 (417) 836-5773 Submission Dates - Submissions for the symposium are due on April 15, 1994. - Notification of acceptance will be given by May 17, 1994. - Material to be included in the working notes of the symposium must be received by August 19, 1994. Organizing Committee Syed S. Ali (chair), Southwest Missouri State University, ssa231f@csm560. smsu.edu or syali@cs.buffalo.edu; Douglas Appelt, SRI International; Lucja Iwanska, Wayne State University; Lenhart Schubert, University of Rochester; Stuart C. Shapiro, State University of New York at Buffalo. Attendance The symposium will be limited to between forty and sixty participants. Working notes will be prepared and distributed to participants in the symposium. A general plenary session, in which the highlights of each symposium will be presented, will be held on November 5, and an informal reception will be held on November 4. In addition to invited participants, a limited number of other interested parties will be able to register in each symposium on a first-come, first- served basis. Registration will be available by mid-July 1994. To obtain registration information write to the AAAI at 445 Burgess Drive, Menlo Park, CA 94025 (fss@aaai.org). Sponsored by the American Association for Artificial Intelligence 445 Burgess Drive, Menlo Park, CA 94025 (415) 328-3123 fss@aaai.org -- Internet: syali@cs.buffalo.edu UUCP: syali@sunybcs.uucp Bitnet: syali%cs.buffalo.edu@ubvm.bitnet Syed Ali, Dept. of CS, 226 Bell Hall, SUNY@Buffalo, Buffalo NY 14260. ``It's not what you do; it's who sees you do it!'' Article 21419 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:21419 comp.ai.edu:1715 Path: honeydew.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!agate!banyan.CS.Berkeley.EDU!marti From: marti@banyan.CS.Berkeley.EDU (Marti Hearst) Newsgroups: comp.ai,comp.ai.edu Subject: CFP: AAAI 1994 Fall Symposium -- Improving Instruction of Intro AI Date: 30 Mar 1994 06:02:40 GMT Organization: University of California, Berkeley Lines: 221 Distribution: inet Message-ID: <2nb4m0$3r6@agate.berkeley.edu> NNTP-Posting-Host: banyan.cs.berkeley.edu Keywords: instruction symposium introductory ai ========================================================================= Call for Participation AAAI 1994 Fall Symposium Series Improving Instruction of Introductory AI (IIIA) November 4-6, 1994 New Orleans, Louisiana Introductory Artificial Intelligence is a notoriously difficult course to teach well. The two most straightforward strategies are to either present a smorgasbord of topics or to focus on one or two central approaches. The first option often yields disjointed and superficial results, whereas the second presents an incorrectly biased picture. Often students come away with a feeling that the subject matter is not coherent and that the field has few significant achievements. Other issues instructors must face are: What should the balance be between the emphasis on cognitive modeling vs. engineering solutions to hard problems? How should one handle the well-known phenomenon of solutions that work no longer being considered to be part of AI? What strategies do instructors take to deal with these issues? What are some underlying themes that can be used to help structure the material? Are there sets of principles that can be used to instruct the material, even if they do not precisely reflect all of the current viewpoints? We propose that the AI community meet and tackle these issues together. The goal of the symposium is to provide an opportunity to discuss these difficult questions, as well as to share successful strategies, problem assignments, instructional programs, and instructional "bloopers." The symposium will be organized as a workshop, although there will be tutorial aspects, allowing participants to learn from the experiences of colleagues who have worked out successful solutions. All attendees will participate in less formal breakout sessions as well as in discussion following presentations. We are soliciting four kinds of contributions, corresponding to four presentation types. Type I A discussion of a successful strategy for instructing introductory AI, based on experience. The descriptions should be centered around a syllabus and must describe how the strategy handles the smorgasbord versus bias problem, i.e., what underlying unifying themes give cohesiveness to the approach while at the same time covering the material well. Accepted descriptions (one to three pages plus the syllabus) will appear in the working notes, and the authors of some of these will be asked to present these ideas in the form of a talk. Those submitting papers on successful overall approaches should also address the following issues, and optionally those listed under Type II as well. 1) What are the basics that must be covered, and how are they integrated into the theme of the course? 2) One strategy is to describe a problem to be solved and then discuss several different techniques that attempt to solve the problem. The inverse strategy describes a technique and then explores how well it performs on different problem types. Which, if either, strategy is used? 3) What kind of curriculum does this fit in to? (E.g., isolated undergraduate semester, part of an intelligent systems series, overview for graduate students, etc.) Type II A position paper addressing at least one of the following issues (one to four pages). Selected position papers will appear in the working notes and some authors will be asked to participate in well-structured, strongly moderated panel discussions (i.e., answers to a small set of questions prepared in advance). Content issues include: 1) What is the role of cognitive motivation, if any? Should there be an emphasis on simulating intelligence, modeling the mind, etc.? 2) What is the role of formalism? 3) Should commercially feasible aspects be addressed, and if so, how? 4) How should we address the question, is it still AI if it can be done? 5) What role should advanced areas (e.g., vision, robotics) play? 6) What kind of hooks should be left for more in-depth courses (e.g., graduate ML, NLP, vision, KR, connectionism, etc.)? 7) What is the role of historical developments? Should this be a structuring theme? Curricular issues include: 1) What kind of department; interaction with other subareas, e.g.: cognitive systems, intelligent systems. 2) What kind of educational program: One semester or quarter overview? A two or three semester series centered around a theme (e.g., KR)? 3) Undergraduate versus graduate: How much should they overlap? Remedial introduction for those who never took undergraduate AI? What are the positive and negative results? Undergrad pedagogical, hands on experience? Graduate reading: primary sources? Teaching college perspective issues include: 1) What are the special issues? 2) How can pooled resources help? Laboratory involvement issues include: 1) Is programming useful or a time waster? Is it better to use and observe existing tools instead? Or is a compromise--modifying existing programs-- best? Innovative ideas? Type III Informative descriptions of programming tools for assignments and as instructional aids. Many potential symposium participants have expressed interest in acquiring a collection of useful programs both for demonstration of AI concepts and for use in homework assignments in lieu of requiring students to spend their time programming. This is an opportunity to advertise tools, videotapes, and other teaching aids. We would like all participants that have used tools that they have found to be useful to list these tools, and all developers of new tools to describe them. For those who have created new tools, we would like to create a repository of same. Type III submissions should address at least one of the following (one to five pages): 1) Descriptions of new educational tools specifically designed for instruction of AI (e.g., The FLAIR project of Papalaskari et al.). 2) Recommendations and pans of existing tools as instructional aids. 3) Proposals for tools or tool construction methodologies that don't exist but should. 4) Tools for empirical exploration (e.g., a tutorial guide to the tools in the machine learning repository, as applied for instructional purposes). Type IV Contributory questions (one to two pages). Those wishing to participate but who do not wish to contribute in one of the previous categories must indicate their interest by describing what issues they are interested in (including those listed above) and suggesting questions to be addressed by the symposium. Descriptions of bloopers--mistakes made that others should be warned away from, are also appropriate. We are especially interested in topics to be discussed by small groups, and questions to be asked of the panelists. Submission Information: Authors may send contributions of more than one type, if desired. Only one document should be submitted. A submitter who wants to make more than one type of submission should simply label each part of the document with the appropriate type (Type I, Type II, etc). Email submissions are strongly preferred. Send one copy, plain text, to: marti@cs.berkeley.edu. PostScript is acceptable if it is accompanied by a plain text version as well; this way diagrams can be included in an electronic form. Those people who cannot send an electronic version should send three hard copies to: Marti Hearst Chair, Symposium on Improving Instruction of Introductory AI Xerox PARC 3333 Coyote Hill Rd Palo Alto, CA 94304 Organizing Committee Marti Hearst (chair), UC Berkeley; Haym Hirsh, Rutgers; Dan Huttenlocher, Cornell; Nils Nilsson, Stanford; Bonnie Webber, University of Pennsylvania; Patrick Winston, MIT. =========================== Sponsored by the American Association for Artificial Intelligence 445 Burgess Drive, Menlo Park, CA 94025 (415) 328-3123 fss@aaai.org Symposia will be limited to between forty and sixty participants. Each participant will be expected to attend a single symposium. Working notes will be prepared and distributed to participants in each symposium. [NB: the IIIA symposium may be made larger if there is a perceived need. --MH] A general plenary session, in which the highlights of each symposium will be presented, will be held on November 5, and an informal reception will be held on November 4. In addition to invited participants, a limited number of other interested parties will be able to register in each symposium on a first-come, first- served basis. Registration will be available by mid-July 1994. To obtain registration information write to the AAAI at 445 Burgess Drive, Menlo Park, CA 94025 (fss@aaai.org). Submission Dates - Submissions for the symposia are due on April 15, 1994. - Notification of acceptance will be given by May 17, 1994. - Material to be included in the working notes of the symposium must be received by August 19, 1994. Partial support for travel expenses will be available to some participants on a need-driven basis. =========================== Article 21419 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:21419 comp.ai.edu:1715 Path: honeydew.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!agate!banyan.CS.Berkeley.EDU!marti From: marti@banyan.CS.Berkeley.EDU (Marti Hearst) Newsgroups: comp.ai,comp.ai.edu Subject: CFP: AAAI 1994 Fall Symposium -- Improving Instruction of Intro AI Date: 30 Mar 1994 06:02:40 GMT Organization: University of California, Berkeley Lines: 221 Distribution: inet Message-ID: <2nb4m0$3r6@agate.berkeley.edu> NNTP-Posting-Host: banyan.cs.berkeley.edu Keywords: instruction symposium introductory ai ========================================================================= Call for Participation AAAI 1994 Fall Symposium Series Improving Instruction of Introductory AI (IIIA) November 4-6, 1994 New Orleans, Louisiana Introductory Artificial Intelligence is a notoriously difficult course to teach well. The two most straightforward strategies are to either present a smorgasbord of topics or to focus on one or two central approaches. The first option often yields disjointed and superficial results, whereas the second presents an incorrectly biased picture. Often students come away with a feeling that the subject matter is not coherent and that the field has few significant achievements. Other issues instructors must face are: What should the balance be between the emphasis on cognitive modeling vs. engineering solutions to hard problems? How should one handle the well-known phenomenon of solutions that work no longer being considered to be part of AI? What strategies do instructors take to deal with these issues? What are some underlying themes that can be used to help structure the material? Are there sets of principles that can be used to instruct the material, even if they do not precisely reflect all of the current viewpoints? We propose that the AI community meet and tackle these issues together. The goal of the symposium is to provide an opportunity to discuss these difficult questions, as well as to share successful strategies, problem assignments, instructional programs, and instructional "bloopers." The symposium will be organized as a workshop, although there will be tutorial aspects, allowing participants to learn from the experiences of colleagues who have worked out successful solutions. All attendees will participate in less formal breakout sessions as well as in discussion following presentations. We are soliciting four kinds of contributions, corresponding to four presentation types. Type I A discussion of a successful strategy for instructing introductory AI, based on experience. The descriptions should be centered around a syllabus and must describe how the strategy handles the smorgasbord versus bias problem, i.e., what underlying unifying themes give cohesiveness to the approach while at the same time covering the material well. Accepted descriptions (one to three pages plus the syllabus) will appear in the working notes, and the authors of some of these will be asked to present these ideas in the form of a talk. Those submitting papers on successful overall approaches should also address the following issues, and optionally those listed under Type II as well. 1) What are the basics that must be covered, and how are they integrated into the theme of the course? 2) One strategy is to describe a problem to be solved and then discuss several different techniques that attempt to solve the problem. The inverse strategy describes a technique and then explores how well it performs on different problem types. Which, if either, strategy is used? 3) What kind of curriculum does this fit in to? (E.g., isolated undergraduate semester, part of an intelligent systems series, overview for graduate students, etc.) Type II A position paper addressing at least one of the following issues (one to four pages). Selected position papers will appear in the working notes and some authors will be asked to participate in well-structured, strongly moderated panel discussions (i.e., answers to a small set of questions prepared in advance). Content issues include: 1) What is the role of cognitive motivation, if any? Should there be an emphasis on simulating intelligence, modeling the mind, etc.? 2) What is the role of formalism? 3) Should commercially feasible aspects be addressed, and if so, how? 4) How should we address the question, is it still AI if it can be done? 5) What role should advanced areas (e.g., vision, robotics) play? 6) What kind of hooks should be left for more in-depth courses (e.g., graduate ML, NLP, vision, KR, connectionism, etc.)? 7) What is the role of historical developments? Should this be a structuring theme? Curricular issues include: 1) What kind of department; interaction with other subareas, e.g.: cognitive systems, intelligent systems. 2) What kind of educational program: One semester or quarter overview? A two or three semester series centered around a theme (e.g., KR)? 3) Undergraduate versus graduate: How much should they overlap? Remedial introduction for those who never took undergraduate AI? What are the positive and negative results? Undergrad pedagogical, hands on experience? Graduate reading: primary sources? Teaching college perspective issues include: 1) What are the special issues? 2) How can pooled resources help? Laboratory involvement issues include: 1) Is programming useful or a time waster? Is it better to use and observe existing tools instead? Or is a compromise--modifying existing programs-- best? Innovative ideas? Type III Informative descriptions of programming tools for assignments and as instructional aids. Many potential symposium participants have expressed interest in acquiring a collection of useful programs both for demonstration of AI concepts and for use in homework assignments in lieu of requiring students to spend their time programming. This is an opportunity to advertise tools, videotapes, and other teaching aids. We would like all participants that have used tools that they have found to be useful to list these tools, and all developers of new tools to describe them. For those who have created new tools, we would like to create a repository of same. Type III submissions should address at least one of the following (one to five pages): 1) Descriptions of new educational tools specifically designed for instruction of AI (e.g., The FLAIR project of Papalaskari et al.). 2) Recommendations and pans of existing tools as instructional aids. 3) Proposals for tools or tool construction methodologies that don't exist but should. 4) Tools for empirical exploration (e.g., a tutorial guide to the tools in the machine learning repository, as applied for instructional purposes). Type IV Contributory questions (one to two pages). Those wishing to participate but who do not wish to contribute in one of the previous categories must indicate their interest by describing what issues they are interested in (including those listed above) and suggesting questions to be addressed by the symposium. Descriptions of bloopers--mistakes made that others should be warned away from, are also appropriate. We are especially interested in topics to be discussed by small groups, and questions to be asked of the panelists. Submission Information: Authors may send contributions of more than one type, if desired. Only one document should be submitted. A submitter who wants to make more than one type of submission should simply label each part of the document with the appropriate type (Type I, Type II, etc). Email submissions are strongly preferred. Send one copy, plain text, to: marti@cs.berkeley.edu. PostScript is acceptable if it is accompanied by a plain text version as well; this way diagrams can be included in an electronic form. Those people who cannot send an electronic version should send three hard copies to: Marti Hearst Chair, Symposium on Improving Instruction of Introductory AI Xerox PARC 3333 Coyote Hill Rd Palo Alto, CA 94304 Organizing Committee Marti Hearst (chair), UC Berkeley; Haym Hirsh, Rutgers; Dan Huttenlocher, Cornell; Nils Nilsson, Stanford; Bonnie Webber, University of Pennsylvania; Patrick Winston, MIT. =========================== Sponsored by the American Association for Artificial Intelligence 445 Burgess Drive, Menlo Park, CA 94025 (415) 328-3123 fss@aaai.org Symposia will be limited to between forty and sixty participants. Each participant will be expected to attend a single symposium. Working notes will be prepared and distributed to participants in each symposium. [NB: the IIIA symposium may be made larger if there is a perceived need. --MH] A general plenary session, in which the highlights of each symposium will be presented, will be held on November 5, and an informal reception will be held on November 4. In addition to invited participants, a limited number of other interested parties will be able to register in each symposium on a first-come, first- served basis. Registration will be available by mid-July 1994. To obtain registration information write to the AAAI at 445 Burgess Drive, Menlo Park, CA 94025 (fss@aaai.org). Submission Dates - Submissions for the symposia are due on April 15, 1994. - Notification of acceptance will be given by May 17, 1994. - Material to be included in the working notes of the symposium must be received by August 19, 1994. Partial support for travel expenses will be available to some participants on a need-driven basis. ===========================