After explicating the need for a large commonsense knowledge base spanning human consensus knowledge, we report on many of the lessons learned over the first five years of attempting its construction. We have come a long way in terms of methodology, representation language, techniques for efficient inferencing, the ontology of the knowledge base, and the environment and infrastructure in which the knowledge base is being built. We describe the evolution of Cyc and its current state and close with a look at our plans and expectations for the coming five years, including an argument for how and why the project might conclude at the end of this time.

AI Magazine, Fall, pages 32-59, copyright 01990, American Association for Artificial Intelligence.

Addison-Wesley.

AI Magazine Volume 10 Number 4 (1989) ( AAAI) generated some controversy. Relative to the discussion of the role of strong syllables in lexical segmentation, Gerry Altmann of CSTR reviewed some of the evidence based on computational studies of large The 1988 Workshop on Cognitive bone. Evidence from human studies computerized lexicons (20,000 Models of Speech Processing was suggested that the spurious word is words). This evidence suggested that held at Park Hotel Fiorelle, Sperlonga, activated, even though in principle it a stressed syllable conveys more Italy, on 16-20 May 1988. Twentyfive would be possible to prevent this activation information about the identity of the participants gathered in this by only accessing the lexicon at word in which it occurs than an small coastal village, where the the offset of some previously found unstressed syllable.

IEEE Computer, pages 100-109, January

Hillis's book, like its bibliography, has something to offer nearly everyone in the computer science community, even those with no background in computer architecture. The book provides a glimpse of what the next generation of computers will be like.

Cambridge, can read this material and gain insight into some of the Massachusetts: The MIT Press, 1985. The treatment is not detailed enough to be used as a text on The Connection Machine introduces a new type of parallel architecture design but it is illuminating and interesting computer which may lead to radically new ways of to read. Once the reader has been introduced to the basics of The author, Daniel Hillis, is the designer of the Connection Machine architecture, the author presents a machine and the founder of Thinking Machines Corporation, description of a prototype called CMl; a machine with a company committed to building "Connection Machines." Hillis discusses the Hillis' book describes the Connection Machine custom VLSI chip, details of the simple processor cells, and and the issues surrounding its design. At made up of thousands, potentially millions, of small, simple, times the Connection Machine appears so different from processors working simultaneously, each with its own current computers that it seems more akin to science fiction tiny memory.

Cambridge, Mass.: MIT Press

AI Magazine, 6(4):65-85

Cooperative distributed problem solving networks are distributed networks of semi-autonomous processing nodes that work together to solve a single problem. The Distributed Vehicle Monitoring Testbed is a flexible and fully-instrumented research tool for empirically evaluating alternative designs for these networks. The testbed simulates a class of a distributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. There are two important aspects to the testbed: (1.) it implements a novel generic architecture for distributed problems solving networks that exploits the use of sophisticated local node control and meta-level control to improve global coherence in network problem solving; (2.) it serves as an example of how a testbed can be engineered to permit the empirical exploration of design issues in knowledge AI systems. The testbed is capable of simulating different degrees of sophistication in problem solving knowledge and focus-of attention mechanisms, for varying the distribution and characteristics of error in its (simulated) input data, and for measuring the progress of problem solving. Node configuration and communication channel characteristics can also be independently varied in the simulated network.