The management information directly to Motor (Dearborn, MI) has opened a systein introduces a new type of data clients to help them minimize risk facility for developing tools and applications structure that encodes the combined and prevent lost sales. Ford's new lab will established in the conventional GDE Systems (San Diego, CA) and develop tools for a variety of engineering indexing system. Development and Engineering Center including vehicle-packaging Xerox (Stamford, CT), a photocopier (ARDEC) (Picatinny, NJ) are using virtual studies, design verification, and a manufacturer, reengineered and validated reality to meet the U.S. Department "walk-up" virtual reality station for its nonproduction-related purchasing of Defense's mandate for efficient designers to evaluate future generations procedure using an of Ford vehicles. The expert system and cost-effective weapons helped Xerox identify a number of system design. The first taking into consideration the workpiece's World Builder (Rochester, NY), a system is an adaptive life simulator shape, the task constraints, company focused on environmental, that exhibits the cardiovascular and the fixture kit.

New network found in any other venue. It is my Collaborative Enterprises (WETICE working groups. John R. Callahan is an assistant professor of computer science in the Department of He received his Ph.D. from the

(Bledsoe 1976). We didn't know we were being We spent a lot of time reading by ourselves, because most of the time the other grades were having their classes. But we DID learn, and had some pretty died on 4 October 1995 of ALS, good teachers (Bledsoe 1976). Woody was one of the and recalls spending "hours just roaming founders of AI, making early contributions in around, sometimes working mathematics pattern recognition and automated reasoning. He continued to make significant contributions When Woody was 12, his father died. It was to AI throughout his long career. His a devastating blow both emotionally and legacy consists not only of his scientific work financially. As Woody recalled, "We were poor but also of several generations of scientists before, but after papa died in January 1934, who learned from Woody the joy of scientific things got worse" (Bledsoe 1976). He and the research and the way to go about it. Woody's rest of his brothers and sisters worked dreary enthusiasm, his perpetual sense of optimism, 10-hour days to make ends meet. He to humanity offered those who knew him the found work in north Texas driving a tractor all hope and comfort that truly good and great night. After a month, he hopped a freight men do exist. He graduated little farm near Maysville, Oklahoma. He moved to Oklahoma to try his took a job as a dishwasher, working 12-hour luck at farming. Woody was the fourth child days 7 days a week. In for his heroic activities in arranging the April, the restaurant owner forced him back transportation of troops across the Rhine into working 12-hour days, which was too in March, 1945. He left the Rhine bridges except the one at Remagen university without saying goodbye and had been destroyed by the retreating German joined the United States Army. Patton's Third Army decided to cross the Rhine by boats near Frankfurt rather than suffer the delay of waiting for bridge construction. Therefore the went to Officer's Candidate School (OCS) Army Corps of Engineers hauled naval By the time he in 1942, he had been promoted to second landing craft (designed for beach landings) lieutenant. While at OCS, Woody had an by truck across Europe to ferry experience that had a profound effect on him: troops across the Rhine. Bledsoe, by then an Army captain, recalls that there was Another experience at OCS at Fort only light enemy fire during the crossing; Belvoir left a lasting impression on me. His first "research" was experimenting army truck. The simple idea opened the flap and said, "Get out here. of backing the trucks into the water, Let's do the map reading." He would later father a to get on with the work, to finish the son, Greg, born in March 1947. It taught me that "if we have to had two more children, Pam and Lance.

LOLA won the Office Cleanup event at the 1995 Robot Competition and Exhibition, held as part of the Fourteenth International Conference on Artificial Intelligence. The event called for a robot to pick up trash in an unstructured environment and sort it such that the recyclable trash winded up in the recycle bin and the regular trash in the trash bin. The only allowable information lola was given beforehand were model-based descriptions of the trash and recyclables, which it located using color vision. Much of LOLA's success can be attributed to the simple, fast algorithms and methods that also model sensor uncertainty. The ideas and design philosophy that went into LOLA borrow heavily from those of previous competitors' to which we are greatly indebted. These methods and ideas are discussed here.

The goal of this workshop was which was held in June 1995 in San istributed artificial intelligence the cooperative solution of "making connections," trying to better Francisco. The DAI Workshop problems in multiagent intelligent understand the connections received financial support from the systems with both computational between DAI and related fields (for American Association for Artificial and human agents. The central problem example, computer-supported cooperative Intelligence as well as the Boeing in DAI is how to achieve coordinated work, group decision support Company. Registration materials for the Thirteenth National Conference on Artificial Intelligence (AAAI-96), the Eighth Innovative Applications of Artificial Intelligence Conference (IAAI-96), and the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) are now available from the AAAI office at ncai@aaai.org Copies of the AAAI-96 registration brochure are being mailed to all AAAI members.

The paper describes an extension of well-founded semantics for logic programs with two types of negation. In this extension information about preferences between rules can be expressed in the logical language and derived dynamically. This is achieved by using a reserved predicate symbol and a naming technique. Conflicts among rules are resolved whenever possible on the basis of derived preference information. The well-founded conclusions of prioritized logic programs can be computed in polynomial time. A legal reasoning example illustrates the usefulness of the approach.

Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic databases with comparable efficiency. We propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks. In the conventional algorithm, new evidence is absorbed in O(1) time and queries are processed in time O(N), where N is the size of the network. We propose an algorithm which, after a preprocessing phase, allows us to answer queries in time O(log N) at the expense of O(log N) time per evidence absorption. The usefulness of sub-linear processing time manifests itself in applications requiring (near) real-time response over large probabilistic databases. We briefly discuss a potential application of dynamic probabilistic reasoning in computational biology.

An impedence controlled manipulandum for human movement studies.

With the exception of (Becker 1992), there has been little attempt to use non-linearity in networks to achieve something a linear network could not. Nonlinear networks, however, are capable of computing more general statistics than those second-order ones involved in decorrelation, and as a consequence they are capable of dealing with signals (and noises) which have detailed higher-order structure. The success of the'H-J' networks at blind separation (Jutten & Herault 1991) suggests that it should be possible to separate statistically independent components, by using learning rules which make use of moments of all orders. This paper takes a principled approach to this problem, by starting with the question of how to maximise the information passed on in nonlinear feed-forward network. Starting with an analysis of a single unit, the approach is extended to a network mapping N inputs to N outputs. In the process, it will be shown that, under certain fairly weak conditions, the N ---. N network forms a minimally redundant encoding ofthe inputs, and that it therefore performs Independent Component Analysis (ICA). 2 Information maximisation The information that output Y contains about input X is defined as: I(Y, X) H(Y) - H(YIX) (1) where H(Y) is the entropy (information) in the output, while H(YIX) is whatever information the output has which didn't come from the input. In the case that we have no noise (or rather, we don't know what is noise and what is signal in the input), the mapping between X and Y is deterministic and H(YIX) has its lowest possible value of

It is well known that axons are neural processes specialized for transmitting information over relatively long distances in the nervous system. Impulsive electrical disturbances known as action potentials are normally initiated near the cell body of a neuron when the voltage across the cell membrane crosses a threshold. These pulses are then propagated with a fairly stereotypical shape at a more or less constant velocity down the length of the axon. Consequently, axons excel at precisely preserving the relative timing of threshold crossing events but do not preserve any of the initial signal shape. Information, then, is presumably encoded in the relative timing of action potentials.