============================================================================== AAAI 1994 Fall Symposium RELEVANCE 4-6 November 1994 The Monteleone Hotel, New Orleans, Louisiana == Call for Participation == With too little information, reasoning and learning systems cannot work effectively. Surprisingly, too much information can also cause the performance of these systems to degrade, in terms of both accuracy and efficiency. It is therefore important to determine what information must be preserved, or more generally, to determine how best to cope with superfluous information. The goal of this workshop is a better understanding of this topic, relevance, with a focus on techniques for improving a system's performance (along some dimension) by ignoring or de-emphasizing irrelevant and superfluous information. These techniques will clearly be of increasing importance as knowledge bases, and learning systems, become more comprehensive to accommodate real-world applications. There are many forms of irrelevancy. In many contexts (including both deduction and induction), the initial theory may include more information than the task requires. Here, the system may perform more effectively if certain irrelevant *facts* (or nodes in a neural net or Bayesian network) are ignored or deleted. In the context of learning, certain *attributes* of each individual sample may be irrelevant in that they will play essentially no role in the eventual classification or clustering. Also, the learner may choose to view certain *samples* to be irrelevant, knowing that they contain essentially no new information. Yet another flavor of irrelevance arises during the course of a general computation: A computing process can ignore certain *intermediate results*, once it has established that they will not contribute to the eventual answer; consider alpha-beta pruning or conspiracy numbers in game-playing and other contexts, or control heuristics in derivation. == Submission Information == Potential attendees should submit a one-page summary of their relevant research, together with a set of their relevant papers (pun unavoidable). People wishing to present material should also submit a 2000 word abstract. We invite papers that deal with any aspect of this topic, including characterizations of irrelevancies, ways of coping with superfluous information, ways of detecting irrelevancies and focusing on relevant information, and so forth; and are particularly interested in studies that suggest ways to improve the efficiency or accuracy of reasoning systems (including question-answerers, planners, diagnosticians, and so forth) or to improve the accuracy, sample complexity, or computational or space requirement of learning processes. We encourage empirical studies and cognitive theories, as well as theoretical results. We prefer plain-text, stand-alone LaTeX or Postscript submissions sent by electronic mail to greiner@learning.scr.siemens.com. Otherwise, please mail three copies to Russell Greiner "Relevance Symposium" Siemens Corporate Research, Inc 755 College Road East Princeton, NJ 08540-6632 In either case, the submission must arrive by 15 Apr 1994. == Important Dates == - Submissions due 15 April 1994 - Notification of acceptance 17 May 1994 - Working notes mailed out 20 Sept 1994 - Fall Symposium Series 4-6 Nov 1994 == Organizing Committee == Russ Greiner (co-chair, Siemens Corporate Research, greiner@learning.scr.siemens.com) Yann Le Cun (AT&T Bell Laboratories) Nick Littlestone (NEC Research Institute) David McAllester (MIT) Judea Pearl (UCLA) Bart Selman (AT&T Bell Laboratories) Devika Subramanian (co-chair, Cornell, devika@cs.cornell.edu) == Attendance == The symposium will be limited to between forty and sixty participants. In addition to invited participants, a limited number of other interested parties will be able to register on a first-come, first-served basis. Registration will be available by mid-July 1994. To obtain registration information, contact AAAI at fss@aaai.org; (415) 328-3123; or 445 Burgess Drive, Menlo Park, CA 94025. == Sponsored by == American Association for Artificial Intelligence as part of the AAAI 1994 Fall Symposium Series.