July Conference Highlights m An AI Art Exhibition will showcase robot weighing in at 300 lbs., from the use of AI in serious works SRI; Bert and Ernie, midget-sized Again this year, AAAI is staging the of art. AIon-Line, five audience-interactive Technologies, untethered and battery with the National Conference on Artificial AI user panels, will offer practical powered, from NASA-JSC; William, a Intelligence, with 19 deployed learning on key business and feel-its-way robot from MIT and JPL; applications selected for presentation organization issues, based on case Flash, a Denning mobile platform from entries from around the world. A series of invited speakers and guidance from MITRE; Flash dimension to the conference, panels will complement the refereed Zorton, a walking machine designed promising give-and-take discussions papers and introduce areas of to compete in the robotic decathlon, about AI in operation. AI research that have unusual from Ecole Polytechnique of Montreal; AAAI-92 offers a series of technical interest and application. The of Southern California/Information The AAAI Robot Rules capture the National Conference is the year's Sciences Institute, and Peter spirit of the competition, indicating, largest meeting ground for those Szolovits, Associate Professor of Computer "It will not be slick, polished...there interested in AI, from scientific, academic, Science at the Massachusetts will be a certain amount of chaos, and business communities. This year's program is particularly There is a serious purpose, diverse, with concentration on research AAAI To Include New Dean noted, "to bring together areas results that bridge the gaps between AI Robotics Competition of AI including those working in perception, the different AI technologies and the AAAI will have its first AI Robotics Highlights, including AAAI-92 National Conference in San facilitate this and to make 34 focused technical sessions, with Jose, California July 12-16, 1992.

In some Winter, 1991) brought a broad nostalgic The principles of statistical pattern mature and highly technical disciplines, smile to my face. I believe that recognition I had employed then are this mode of thought can be I am the unnamed Yale junior faculty very general indeed. Unfortunately, this is not member to whose work Prof. Schank very principles form the basis of all the case in our chosen pursuit of the alluded. Perhaps the intervening contemporary speech recognition essence of mind which should be years have eradicated his memory of systems which, in their best incarnations seen as a young and interdisciplinary my name or, more likely, he wished here at Bell Laboratories and enterprise. I, transcribing fluent speech of virtually organisms, she was not constrained however, fully mindful of Oscar any speaker talking about a specific by the academic boundaries that Wilde's observation that the only topic and using a vocabulary of thousands have since evolved.

. It is argued that Situated Agents should be designed using a unitaryon-line computational model. The Constraint Net model of Zhang and Mackworth satisfiesthat requirement. Two systems for situated perception built in our laboratory are describedto illustrate the new approach: one for visual monitoring of a robot’s arm, the other forreal-time visual control of multiple robots competing and cooperating in a dynamic world.First proposal for robot soccer.Proc. VI-92, 1992. later published in a book Computer Vision: System, Theory, and Applications, pages 1-13, World Scientific Press, Singapore, 1993.

In AAAI-92, pp. 816–822.

Tech. rep. Papers from the AAAI Robot Competition, RC-92-01, American Association for Artificial Intelligence.

In ECAI-92, pp. 689–693.

A high speed implementation of the CMAC neural network was designed using dedicated CMOS logic. This technology was then used to implement two general purpose CMAC associative memory boards for the VME bus. Each board implements up to 8 independent CMAC networks with a total of one million adjustable weights. Each CMAC network can be configured to have from 1 to 512 integer inputs and from 1 to 8 integer outputs. Response times for typical CMAC networks are well below 1 millisecond, making the networks sufficiently fast for most robot control problems, and many pattern recognition and signal processing problems.

There have been very few demonstrations ofthe application ofVLSI neural networks to real world problems. Yet there are many signal processing, pattern recognition or optimization problems where a large number of competing hypotheses need to be explored in parallel, most often in real time. The massive parallelism of VLSI neural network devices, with one multiplier circuit per synapse, is ideally suited to such problems. In this paper, we present preliminary results from our design for a real time robot navigation system based on VLSI neural network modules.

A high speed implementation of the CMAC neural network was designed using dedicated CMOS logic. This technology was then used to implement two general purpose CMAC associative memory boards for the VME bus. Each board implements up to 8 independent CMAC networks with a total of one million adjustable weights. Each CMAC network can be configured to have from 1 to 512 integer inputs and from 1 to 8 integer outputs. Response times for typical CMAC networks are well below 1 millisecond, making the networks sufficiently fast for most robot control problems, and many pattern recognition and signal processing problems.

A high speed implementation of the CMAC neural network was designed using dedicated CMOS logic. This technology was then used to implement two general purpose CMAC associative memory boards for the VME bus. Each board implements up to 8 independent CMAC networks with a total of one million adjustable weights. Each CMAC network can be configured to have from 1 to 512 integer inputs and from 1 to 8 integer outputs. Response times for typical CMAC networks are well below 1 millisecond, making the networks sufficiently fast for most robot control problems, and many pattern recognition and signal processing problems.