The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six 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?

A simulated (Houston, Tex.) has selected Telepresence technology allows scientists The Consolidated Communications from the Advanced Technology Program signed a strategic alliance agreement Facility's Element Manager at the National Institute of Standards with Gensym (Cambridge, Mass.) to will allow data communications and Technology. The grant will use Gensym's G2 real-time expert system system operators to remotely configure, support Kurzweil AI's development of development tool. Chevron control and monitor the operation a spoken-language interface capable installations are using G2 to intelligently of the front-end processor, providing of controlling PC software applications monitor energy management simultaneous support for through natural language and process simulation in conjunction multiple manned space flight missions, instruction in combination with a with other systems. Logica Cambridge (Cambridge, Developers at Georgia Tech AT&T Universal Card Services England) is developing a virtual reality (Atlanta, Ga.) have designed a neural (Jacksonville, Fla.) has signed a multiyear application to improve presentation network modeling, control and diagnostic agreement with HNC (San Diego, of data for air traffic controllers. Falcon uses see the heights of different aircraft, linked to sensors and other data neural network technology to learn rather than just the altitudes displayed sources on the factory floor, the neural and identify unusual transaction pat-numerically.

The principal investigator (PI)-IN-A-BOX knowledge based system helps astronauts perform science experiments in space. These experiments are typically costly to devise and build and often are difficult to perform. Further, the space laboratory environment is unique; ever changing; hectic; and, therefore, stressful. The environment requires quick, correct reactions to events over a wide range of experiments and disciplines, including ones distant from an astronaut's main science specialty. This environment suggests the use of advanced techniques for data collection, analysis, and decision making to maximize the value of the research performed. PI-IN-A-BOX aids astronauts with quick-look data collection, reduction, and analysis as well as equipment diagnosis and troubleshooting, procedural reminders, and suggestions for high-value departures from the preplanned experiment protocol. The astronauts have direct access to the system, which is hosted on a portable computer in the Space Lab module. The system is in use on the ground for mission training and was used in flight during the October 1993 space life sciences 2 (SLS-2) shuttle mission.

To integrate robotics into society, it is first necessary to measure and analyze current societal responses to areas within robotics. This article is the second in a continuing series of reports on the societal effects of various aspects of robotics. In my previous article, I discussed the problems of sensor abuse and outlined a program of treatment. However, despite the wide dissemination of that article, there are still numerous empty beds at the Susan Calvin Clinic for the Prevention of Sensor Abuse. Sensor abuse continues unabated despite strong evidence that there is a better way. In this article, I explore the age-old question, Why does the robotics community look down on efficient sensing systems?

The information in this article was originally presented as a keynote invited talk by Susan Lederman at the Thirteenth International Joint Conference on Artificial Intelligence in Chambery, France; it is based primarily on a joint research program that we conducted. We explain how the scientific study of biological systems offers a complementary approach to the more formal analytic methods favored by roboticists; such study is also relevant to a number of classical problems addressed by the AI field. We offer an example of the scientific approach that is based on a selection of our experiments and empirically driven theoretical work on human haptic (tactual) object processing; the nature and role of active manual exploration is of particular concern. We further suggest how this program with humans can be modified and extended to guide the development of highlevel manual exploration strategies for robots equipped with a haptic perceptual system.

He knew the challenges opinion, as one of the premier natural language were great and would require the research groups in the world. He gave efforts of many people. He had a genius for one of us (Barbara Grosz) her first AI job, even bringing these people together. In doing so, he took a of people who had known Don over the risk of a magnitude that she fully appreciated years to send us reminiscences. Although only years later when she herself was hiring each person's story differed, a striking commonality research associates.

Dartnall, Terry, see Kim, Steven Davis, Randall; Shrobe, Howard; and Szolovits, Peter. What Is a Knowledge 1992 AAAI Robot Exhibition and Competition Leonard, Lisa. Dean, Thomas; and Bonasso, R. Capture and Use, The, see Lee, Jintae Technologies, see Barachini, Franz Cannel Versus Flakey: A Comparison of Dean, Tom, see Joskowicz, Leo. Reasoning Dorr, Bonnie J. Building Lexicons for see Tanner, Steve with Diagrammatic Representations: A Machine Translation: 1993 Spring Anick, Peter; and Simoudis, Evange-Report on the Spring Symposium. Retrieval: 1993 Spring Symposium Charniak, Eugene, see Goldman, Drummond, Mark, see Lansky, Amy Report.

The competition, rules, coordinating the setup and Technologies, showed off a unique which attracted teams from administration of the contest, and global-positioning system using a many of the top mobile robotics trying to cope with the needs of the robot-mounted revolving laser and research laboratories in the United 15 teams that put so much energy three or more stationary receivers. States (see side bar), was first proposed into their entries. This article reports Still, many teams suffered frustrating by Thomas Dean and held at some of the experiences I had in failures in hardware and especially the 1992 NCAI conference. Dean's helping to design and run the contest software, leading to a general lack concept was to further the research and some reflections, drawn of sleep and noticeable exhaustion into the skills such robots from post mortem abstracts written among the contestants by Monday need--sensing, interpretation, planning, by the competitors, on the relation of night, the day before the contest. I and reacting--by bringing the contest to current research efforts know this from personal experience: together interested parties in a cooperative in mobile robotics.

We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that affect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some training data and the accuracy of the tree on test data. The experiments were performed on a massively parallel Maspar computer. The results of the experiments on several artificial and two real world problems indicate that, for many of the problems investigated, smaller consistent decision trees are on average less accurate than the average accuracy of slightly larger trees.

This book (originally published in 1994 by Ellis Horwood) is now out of print. The copyright now resides with the editors who have decided to make the material freely available on the web.This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.