Uncertainty Digest Tuesday, January 24th 1992 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~ The uncertainty list a means for researchers in uncertain reasoning ~ to exchange information, such as conference announcements, articles, ~ reviews, and software. ~ ~ ~ Please send all submissions and correspondence, as well as requests ~ for changes to the mailing list to agosta@sumex-aim.stanford.edu. ~ Archives may be found by anonymous ftp to sumex-aim.stanford.edu ~ in /var/ftp/pub/unc ~ ~ ~ This list is maintained by John Mark Agosta, 415/965-1990 ~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Today's Topics: AUAI Uncertainty Conference, Final Call AI in Medicine Journal, Special Issue Call for Papers New Book: Representing and Reasoning with Probabilistic Knowledge _________________________________________________________________________ Gentle uncertainty researcher: Note in the announcements that the papers deadline for this summer's conference is a few weeks off. The AI in Medicine papers deadline is June 1st, mercifully distant from the summer's conferences deadlines. As usual, conference and journal calls, general announcements, abstracts of new articles, and books, are solicited for the digest. Also, larger items, such as articles and code can be submitted for the ftp-able archive. Please attach a text description with them that can be posted in the digest. I am happy that the signal to noise in this list is high, directly reflecting the items received, and not due to my editing out submissions. Much thanks to all who took the time to submitt. -jma _________________________________________________________________________ Subject: AUAI Uncertainty Conference Final Call for papers. Date: Tue, 14 Jan 92 11:47:49 PST From: dambrosi@turing.CS.ORST.EDU Final Call for Papers Deadline for submission: Feb. 14, 1992!!! THE EIGHTH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE Stanford, California July 17-19, 1992 (following AAAI-92) The eighth annual Conference on Uncertainty in AI is devoted to the advance of artificial intelligence methods expressly accounting for uncertainty in beliefs. The conference's scope covers the full gamut of approaches to automated and interactive reasoning and decision making under uncertainty, including both qualitative and numeric methods. We invite original contributions on fundamental theoretical issues, on computational techniques for uncertain reasoning, and on novel applications of such theories and technologies to challenging problem-solving tasks. Topics of particular interest include: - Foundations of uncertainty concepts - Representations of uncertain knowledge and their semantics - Automated planning and decision making under uncertainty - Abduction and diagnosis - Algorithms for uncertain inference - Control of reasoning and real-time architectures - Construction of uncertainty models from experts, data, or knowledge bases - Pooling of uncertain evidence - Belief updating and inconsistency handling in uncertain knowledge bases - Explanation and summarization of uncertain information - Engineering principles for applications of uncertain reasoning Papers will be carefully refereed for originality, significance, technical soundness, and clarity of exposition. Papers may be accepted for presentation in plenary or poster sessions. All accepted papers will be included in the published proceedings. Outstanding student papers may be selected for special distinction. Five copies of each paper should be sent to reach one of the Program Co-Chairs by February 14, 1992. The first page should include a descriptive title, the names, addresses, and student status of all authors, a brief abstract, and salient keywords or other topic indicators. Acceptance notices will be sent by April 3, 1992, and final camera-ready papers, incorporating reviewers' suggestions, will be due approximately five weeks later. There will be an eight-page limit on proceedings papers, with a few extra pages available for a fee. Program Co-Chairs (paper submissions): Michael P. Wellman WL/AAA-1 Bldg 620, Room S1J68 Wright-Patterson AFB, OH 45433 USA tel: (513) 255-5800, fax: (513) 476-4052 e-mail: wellman@wl.wpafb.af.mil Didier Dubois I. R. I. T., Universite Paul Sabatier 118 route de Narbonne 31062 Toulouse Cedex, France tel: (+33) 61.55.63.31, fax: (+33) 61.55.62.39 e-mail: dubois@irit.fr _______________________________________________________________ From: Gregory Cooper Date: Wed, 15 Jan 1992 18:39:43 PST Subject: AI in Medicine Journal, Special Issue Call for Papers ARTIFICIAL INTELLIGENCE IN MEDICINE JOURNAL Special Issue on Probabilistic and Decision-Theoretic Systems in Medicine Papers are sought for a special issue of the journal Artificial Intelligence in Medicine on the use of probability and decision theory to develop medical decision support systems. The term "medical decision support system" is intended to be interpreted broadly. Papers should contain original research contributions. Both theoretical and applied work are sought. Topics of specific interest include: o Acquisition of decision-theoretic models from medical experts. Acquisition of probabilities from medical experts and/or databases. o Theory and application of Bayesian belief networks, influence diagrams, as well as other representations. The integration of representations and/or inference methods. o Diagnosis and data gathering o Decision-theoretic therapy planning o Probabilistic causality o Probabilistic temporal reasoning o Decision making in dynamic medical environments o Efficient inference algorithms o Control of probabilistic and decision-theoretic inference o Machine-generated explanations of the application of probabilistic and decision-theoretic models o Results of applying a probabilistic or decision-theoretic system in a medical area The above list of topics of interest is not intended to be exhaustive. Papers will be refereed for their significance, originality, quality, and clarity. Three copies of a submitted paper must arrive at the address below by June 1, 1992. The author(s) should attach the following information to each copy of the paper: postal address, email address, telephone number, and fax number. Papers must follow the format specified by the Guide for Authors of the Artificial Intelligence in Medicine journal. The Guide for Authors is available from the address below. Please address correspondence and send papers to the special issue editor at the following address: Greg Cooper B50A Lothrop Hall 190 Lothrop Street University of Pittsburgh Pittsburgh, PA 15261 email: gfc@med.pitt.edu phone: (412) 648-3190 fax: (412) 648-3126 _________________________________________________________________________ Date: Fri, 17 Jan 92 12:25:59 -0500 From: Fahiem Bacchus Subject: New Book: Representing and Reasoning with Probabilistic Knowledge Representing and Reasoning with Probabilistic Knowledge by Fahiem Bacchus, The MIT Press, 233 pages, ISBN 0262-02317-2 $29.95 (U.S.) ============================================================ Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular interest to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general. The book draws attention to two distinct notions of probability---one involving degrees of belief, e.g., "I think that the probability of the Chicago Bulls winning their next game is 0.9," the other involving statistics, e.g., "the Chicago Bulls have won 75% of their games so far this season." Two distinct logics are constructed with different semantics suitable for representing each type of probability. These logics have the ability to represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilties. Furthermore, they can represent first-order assertions: they are first-order probabilistic logics. Also presented are powerful proof theories which give a formal specification for a class of reasoning which subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI. Using the new tools a system of direct inference is also proposed which connects an agent's statistical knowledge to its degrees of belief. The book demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning. ============================================================ end of uncertainty list, 24 Jan 1992