The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems.

General Electric's Research and Elscint (Hackensack, NJ), a manufacturer Johnson Controls (Milwaukee, WI) Development Center (Schenectady, of medical imaging systems, has has begun deployment of a knowledge-based NY) has developed an expert system begun offering its customers a service engineering application which is being used to increase the option based on expert systems. The to increase the productivity of the speed of design of new jet engines, MasterMind system delivers troubleshooting engineering design function. The system, called Engineous, on laptop or desktop computers. The General (Menlo Park, CA), is conveyor for further processing. It problems and recommends solutions objects have become rotated.

The Fredkin Challenge in AI Research and Applications at Chandrasekaran, B. see nlso Hendler, Match, 2(2): Summer 1981, 30-33.

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Integrated Language and Vision Systems, Scholarship Travel Program If you are interested in assisting AAAI at the national conference, New Mexico State University, Continued please contact AAAI at volunteer Dec. 1991 AAAI announces the continuation of @aaai.org. All inquiries should 1991 IFIP/KR Workshop its scholarship travel program for students include your name, address, telephone, Eleventh International Workshop on who want to attend the National advisor's name, and email Distributed Artificial Intelligence, Conference on Artificial Intelligence address. All requests to volunteer at Glen Arbor, Michigan, February 1992 in San Jose, California, 12-17 July AAAI-92 must be received by the 1992. First International Conference on and (2) are members of April 3 AAAI-92 Scholarship AI Planning Systems, University of AAAI. In addition, repeat scholarship Application Deadline Maryland, June 1992 applicants must have fulfilled the April 29 Al Magazine Summer Issue The Third International Conference volunteer and reporting requirements Calendar Deadline on Principles of Knowledge Representation for previous awards.

The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems.

My Ph.D. thesis (Ward 1992, 1991)1 addressed the task of generating natural language utterances. It was motivated by two difficulties in scaling up existing generators. Current generators only accept input that are relatively poor in information, such as feature structures or lists of propositions; they are unable to deal with input rich in information, as one might expect from, for example, an expert system with a complete model of its domain or a natural language understander with good inference ability. Current generators also have a very restricted knowledge of language -- indeed, they succeed largely because they have few syntactic or lexical options available (McDonald 1987) -- and they are unable to cope with more knowledge because they deal with interactions among the various possible choices only as special cases. To address these and other issues, I built a system called FIG (flexible incremental generator). FIG is based on a single associative network that encodes lexical knowledge, syntactic knowledge, and world knowledge. Computation is done by spreading activation across the network, supplemented with a small amount of symbolic processing. Thus, FIG is a spreading activation or structured connectionist system (Feldman et al. 1988).

Human error is an Criticism should not be querulous, and umes of fast-changing increasingly important wasting, all knife and root puller, but guiding, sensory data that and addressable instructive, inspiring, a South wind, one needs to process concern in modernday not an East wind. Most institutions), and the automation that technology represents accidents waiting to surrounds us (for example, unfriendly computers happen. For example, in the Challenger explosion, We get by because humans excel at coping. the shortcomings of the O-rings had been High-technology accidents occur because known for several years. What feedback hundreds of alarms simultaneously all contributed strategy (for example, story telling, first-principle to the disaster. Likewise, when the lecturing) will most constructively correct British fleet was sent to defend the Falkland the human error? It was at this differences. However, there are no point that the Argentines released their missile models there or in the AI literature of errors and sank an unsuspecting British ship. The operator had The errors result from proficient task performers no inkling of the ramifications of the system practicing in a natural environment; they designs under the current operating conditions. New error and critiquing models operator has virtually no way out. The remarkable need to capture and reflect this difference. We computer-aided design (ICAD) to mitigate begin by examining the design process and such problems. Specifically, we examine the the cognitive difficulties it poses. The designer uses a interference problems are also increasingly variety of cognitive operators to generate a evident on civilian automobiles, airplanes, design, test it under various conditions, refine and ships that cram telephones, radios, computers, it until a stopping rule is reached, and then radar devices, and other electromagnetically store the design as a prototype or analog to incompatible devices into close help start a new process for the next design proximity. The design process is sufficiently complex domain are relevant to all engineering design that a correct and complete design applications that must factor any operational simply cannot be deduced from starting conditions (or manufacturability, sales, or other downstream) or simulation model results.

This paper examines whether temporal difference methods for training connectionist networks, such as Sutton's TD(λ) algorithm, can be successfully applied to complex real-world problems. A number of important practical issues are identified and discussed from a general theoretical perspective. These practical issues are then examined in the context of a case study in which TD(λ) is applied to learning the game of backgammon from the outcome of self-play. This is apparently the first application of this algorithm to a complex non-trivial task. It is found that, with zero knowledge built in, the network is able to learn from scratch to play the entire game at a fairly strong intermediate level of performance, which is clearly better than conventional commercial programs, and which in fact surpasses comparable networks trained on a massive human expert data set.

The title of this paper, "On Seeing Robots", leaves substantial scope for playful exploration. The simple ambiguity is, of course, between describing robots that see their worlds and systems that see robots. These categories are not exclusive: I also combine them and discuss robots that see robots and even robots that see themselves. Furthermore, the title is designed to echo, and pay homage to, a classic vision paper entitled "On Seeing Things" by Max Clowes [1] as I have done once before [2]. But the context, the arguments and the conclusions are new; the comparison is used explicitly here to show the difference between the classical approach and an emerging situated approach to robotic perception. The most important reading of the title is that the paper is about how we see robots; it is about the computational paradigms, the assumptions, the architectures and the tools we use to design and build robots.