KL-ONE is a system for representing knowledge in Artificial Intelligence programs. It has been developed and refined over a long period and has been used in both basic research and implemented knowledge-based systems in a number of places in the AI community. Here we present the kernel ideas of KL-ONE, emphasizing its ability to form complex structured descriptions. In addition to detailing all of KL-ONE's description-forming structures, we discuss a bit of the philosophy underlying the system, highlight notions of taxonomy and classification that are central to it, and include an extended example of the use of KL-ONE and its classifier in a recognition task. This research was supported in part by the Defense Advanced Research Projects Agency under Contract N00014-77-C-0378. Views and conclusions contained in this paper are the authors' and should not be interpreted as representing the official opinion or policy of DARPA, the U.S. Government, or any person or agency connected with them.

The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadth-first search requires too much space and depth-first search can use too much time and doesn't always find a cheapest path. A depth-first iterative-deepening algorithm is shown to be asymptotically optimal along all three dimensions for exponential tree searches. The algorithm has been used successfully in chess programs, has been effectively combined with bi-directional search, and has been applied to best-first heuristic search as well. This heuristic depth-first iterative-deepening algorithm is the only known algorithm that is capable of finding optimal solutions to randomly generated instances of the Fifteen Puzzle within practical resource limits.

Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. Direct applications include economics, medicine, remote sensing, sedimentology, and signal detection (pattern recognition). Also describes indirect applications--in outlier models, density estimation, Bayesian and empirical Bayes analysis, and robustness studies. Goes on to cover mathematical concepts such as identifiability and information, and the inferential problems associated with data from a mixture. Approximate sequential methods are developed here, in order to deal with estimation difficulties and engineering applications.

Communications of the ACM, October 1985.

Review of A Practical Guide to Designing Expert Systems

Letters to the Editor

The initial results from a few AI research projects in statistics have been quite interesting to statisticians: Feasibility demonstration systems have been built at Stanford University, AT-T bell Laboratories, and the University of Edinburgh. Several more design studies have been completed. A conference devoted to expert systems in statistics was sponsored by the Royal Statistical Society. On the other hand, statistic as a domain may be of particular interest to AI researchers, for it offers both tasks well suited to current AI capabilities and tasks requiring development of new AI techniques.

This article describes one person's experience in coming from an academic environment to work at Digital Equipment Corporation. The author feels his own experience has paralleled the transfer of AI technology from academia to industry, where AI researchers must live up to very different expectations, but also enjoy very different rewards. This article covers the historical background of DEC's involvement with AI, the development of R1- known internally and henceforth in this article as XCON-and DEC's experiences with it and its consequences. Finally, the article offers advice for other corporations planning to develop their own capabilities in AI.

Jim Kornell, Robert Park, Christopher Dungan, Joop Schopman, David Drager, Nils J. Nilsson, Marty Kalin, John Gavin, Bernard Meltzer, Robert Salmansohn, Keith McCammon, Loren Martindale Abstract Subjects include AI's impact on employment, the AAAI conference, a response to McCarthy's Presidential Message, AI going public, and computerless expert systems. Subjects include AI's impact on employment, the AAAI conference, a response to McCarthy's Presidential Message, AI going public, and computerless expert systems.

Schlumberger is a large, multinational corporation concerned primarily with the measurement, collection, and interpretation of data. For the past fifty years, most of the activities have been related to hydrocarbon exploration. The efficient location and production of hydrocarbons from an underground formation requires a great deal of knowledge about the formation, ranging in scale from the size and shape of the rock's pore spaces to the size and shape of the entire reservoir. Schlumberger provides its clients with two types of information: measurements, called logs, of the petrophysical properties of the rock around the borehole, such as its electrical, acoustical, and radioactive characteristics; and in terpretations of these logs in terms of geophysical properties such as porosity and mineral composition.