KL-ONE lets one define and use a class of descriptive terms called Concepts, where each Concept denotes a set of objects A subsumption relation between Concepts is defined which is related to set inclusion by way of a semantics for Concepts. This subsumption relation defines a partial order on Concepts, and KL-ONE organizes all Concepts into a taxonomy that reflects this partial order. Classification is a process that takes a new Concept and determines other Concepts that either subsume it or that it subsumes, thereby determining the location for the new Concept within a given taxonomy. We discuss these issues and demonstrate some uses of the classification algorithm. KL-ONE is a knowledge representation system developed at Bolt Beranek and Newman over the past few years (see [Brachman 77, Brachman 79, Schmolze 82. Sidner 81]), that grew out of semantic network formalisms. The primary unit of information in KL-ONE is called a Concept, which denotes a set of objects. A Concept has a set of (syntactic) components, each denoting a property that must be true of each member of the set denoted by the Concept.

The importance. of spatial and other metaphors is demonstrated.

Swartout accrue from such an endeavor?

The first is to have an adequate model of the cognitive state of other agents. The second is to form plans under the constraint of resource limitations: i.e., an agent does not always have an infinite amount of time to sit and think of plans while the world changes under him; he must act. These two problems are obviously interlinked since, to have a realistic model of the cognitive states of other agents, who are presumably similar to himself, an agent must reason about the resource limitations they are subject to in reasoning about the world. In this paper we address both problems with reference to AI planning system robots and one part of their cognitive state, namely beliefs. Our goal is to pursue what might be called robot psychology: to construct a plausible model of robot beliefs by examining robots' internal representations of the world.

An initial description ought to be as compact as possible, and its elements should correspond as closely as possible to meaningful objects or events in the signal-forming process. Frequently, local extrema in the signal and its derivatives-- and intervals bounded by extrema--are particularly appropriate descriptive primitives: although local and closely tied to the signal data, these events often have direct semantic interpretations, e.g. as edges in images. A description that characterizes a signal by its extrema and those of its first few derivatives is a qualitative description of exactly the kind we were taught to use in elementary calculus to "sketch" a function. A great deal of effort has been expended to obtain this kind of primitive qualitative description (for overviews of this literature, see [1,2,3].) and the problem has proved extremely difficult. The problem of scale has emerged consistently as a fundamental source of difficulty, because the events we perceive and find meaningful vary enormously in size and extent. The problem is not so much to eliminate fine-scale noise, as to separate events at different scales arising from distinct physical processes.[4]

Reading, Mass.: Addison-Wesley.

See also: C.I.P. #425, Departments of Computer Science and Psychology, Carnegie-Mellon University, 1980In Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (Eds), Machine Learning, An Artificial Intelligence Approach, Tioga Press, Palo Alto, CA

In Proceedings of the Eighth IJCAI, pages 272-279. IJCAI, Karlsruhe, West Germany, August, 1983. Volume 1.

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness to a user. This paper introduces the Interviewer/Reasoner model, which helps to reduce this conflict. This model partitions an intelligent system into two asynchronous components. The Interviewer's primary function is to gather data while providing an acceptable response time to the user. The Reasoner does most of the symbolic computation for the system. This paper describes the implementation of the model in both timesharing and personal workstation environments, and uses the ONCOCIN system as an example.

I apologise for any mistakes or misinterpretations I may therefore have made. In October 1981,.Japan announced a national project to develop highly innovative computer systems for the 199Os, with the title "Fifth Generation Computer Systems " This paper is a personal view of that project, The fifth generation plan its significance, and reactions to it. In late 1978 the Japanese Ministry of International Trade THIS PAPER PRESENTS a personal view of the Japanese and Industry (MITI) gave ETL the task of defining a project Fifth Generation Computer Systems project.