About a year ago. I was approached by Phoebe Huang of Comtex Scientific Corporation who hoped that I would help devise a dramatically expanded index for topics in AI to aid Comtex in indexing the series of AI memos and reports that they had been gathering. Comtex had tried to get the ACM to expand and update its classification. But was told that ACM had just revised the listing two years ago or so ago, and did not intend to revise it again for a while: even if they did. The revision might require a year or more to complete. Comtex wanted the new classification within six to eight weeks. I agreed to take on the task, thinking it wouldn't be too hard. The major decision I had to make was whether to use the existing ACM index scheme and add to it, or start with a fresh sheet of paper and devise my own. I decided to stick with ACM's top two levels, only adding, not modifying, major headings.

What are the powerful new ideas in knowledge based design? What important research issues require further investigation? Perhaps the key research problem in AI-based design for the 1980's is to develop better models of the design process. A comprehensive model of design should address the following aspects of the design process:the state of the design ; the goal structure of the design process;design decisions; rationales for design decisions; control of the design process; and the role of learning in design. This article presents some of the most important ideas emerging from current AI research on design especially ideas for better models design. It is organized into sections dealing with each of the aspects of design listed above.

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.

Given that the knowledge-based system relies on these inferences in the midst of its operation (i.e., its diagnosis, planning, or whatever), their computational tractability is an important concern. Here we present evidence as to how the cost of computing one kind of inference is directly related to the expressiveness of the representation language.

Programs that make decisions need mechanisms for representing and reasoning about the desirability of the possible consequences of their choices. This work is an exploration of preference models based on utility theory. The framework presented is distinguished by a qualitative view of preferences and a knowledge-based approach to the application of utility theory. The design for a comprehensive preference modeler is implemented in part by the U tility R easoning P ackage (URP), a collection of facilities for constructing and analyzing preference models. Qualitative mathematical reasoning techniques are employed to develop partial specifications of single-attribute utility functions from qualitative preference assertions.

AI Magazine, 6 (1), 32-39.

The AI field needs major breakthroughs in its thinking to achieve continuous, sensory-gathered, machine learning from the environment on unlimited subjects. The way motivate such dramatic progress is to articulate and endorse research goals for machine behavior so ambitious that limited-domain, problemsolving knowledge representation methods are disqualified at the outset, thus forcing ourselves to produce valuable new "thoughtware." After exploring why the tendency to associate intelligence with problem-solving may be a mental roadblock to further progress in AI science, some preliminary thinking tools are introduced more suitable for sensory learning machine research. These include lifelong sensorimotor data streams, representation as a symbolic recording process, knowledge transmission, and the totality of knowledge.

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. Since log interpretation is expert skill, the emergence of expert systems technology prompted Schlumberger's initial interest in Artificial Intelligence. Our first full- scale attempt at a commercial-quality expert system was the Dipmeter Advisor. Following these initial efforts, Schlumberger has expanded its Artificial Intelligence activities, and is now engaged in both basic and applied research in a wide variety of areas.

The AI field needs major breakthroughs in its thinking to achieve continuous, sensory-gathered, machine learning from the environment on unlimited subjects. The way motivate such dramatic progress is to articulate and endorse research goals for machine behavior so ambitious that limited-domain, problemsolving knowledge representation methods are disqualified at the outset, thus forcing ourselves to produce valuable new "thoughtware." After exploring why the tendency to associate intelligence with problem-solving may be a mental roadblock to further progress in AI science, some preliminary thinking tools are introduced more suitable for sensory learning machine research. These include lifelong sensorimotor data streams, representation as a symbolic recording process, knowledge transmission, and the totality of knowledge.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented.