AAAI 1993 Fall Symposium Reports

Copyright © 1994, AAAI. 0738-4602-1994 / $2.00 of theorem provers for nonstandard logics in AI systems. The logics considered included modal logics, many-valued logics, autoepistemic logics, conditional logics, argument-based defeasible reasoning, probabilistic logics, default logic, transaction logic, defeasible Prolog, and temporal logics. Some of the applications were reasoning about actions, commonsense reasoning involved in the use of natural language, and the specification and verification of the properties of a robot. The symposium began with an invited talk by Melvin Fitting entitled “A Many-Valued Modal Logic, with a Curious Tableau System.’’ The rest of the symposium consisted of paper presentations, a tutorial, and three panel sessions. On the afternoon of the first day, there was a session consisting of 10 short talks. The audience was asked to submit ballots indicating their top three choices for talks to be heard in a longer form on the last day. The final day began with the presentations by the winners from the series of short talks. Siani Baker spoke on automated deduction in arithmetic with the omega rule. An epistemic logic with quantification over names was presented by Andrew Haas. Jana Koehler spoke on a planning system that uses temporal logic. A method for reasoning with indefinite knowledge was presented by Thorne McCarty. Jeff Pelletier spoke on semantic tableau methods for modal logics. In many nonstandard logics (unlike classical first-order logic), the set of conclusions warranted from a set of premises is not, in general, recursively enumerable. Some of the ■ The American Association for Artificial Intelligence held its 1993 Fall Symposium Series on October 22–24 in Raleigh, North Carolina. This article contains summaries of the five symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?