See Steamer: an Interactive Inspectable Samulation-Based Reid.

Tin Nguyen performed the work contained in the article "Knowledge Base Verification" while at Lockheed and is currently working for Bell Northern Research as a member of the research staff. Deanne Pecora, a staff engineer with the Lockheed Artificial Intelligence Center, 2710 Sand Hill Road, Menlo Park, California 94025, is working on Rick Brigs, author of "Knowledge Representation and Inference in Sanskrit: A applying knowledge-based systems to Review of the First National Conference," is a senior engineer at Delfin Systems, real problems. She is a coauthor of 1349 Moffett Park Drive, Sunnyvale, California 94089. Briggs is currently working'Knowledge Base Verification." Walt Perkins, coauthor of "Knowledge Base Verification" is a consulting scientist Lindley Darden, who wrote "Viewing the History of Science as Compiled Hindsight," with the Lockheed Artificial is an associate professor in the departments of philosophy and history and Intelligence Center, 2710 Sand Hill a member of the graduate faculty in the Committee on the History and Philosophy Road, Menlo Park, California 94025 of Science at the University of Maryland, College Park. She is currently and the principal developer of the serving in the second year of a halftime research appointment at the University Lockheed expert system. of Maryland Institute for Advanced Computer Studies. Her mailing address is Department of Philosophy, University of Maryland, College Park, Maryland David Prerau is a principal member of 20742. The primary responsibility is to lead the author of "The 1985 Workshop on Distributed Artificial Intelligence, he is currently development of major expert systems working in the area of distributed artificial intelligence and is organizing with high corporate payoff and impact.

A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples -- one in English and one in Chinese -- are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms.

In his recent article in AI Magazine, "AI prepares for 2001," Nils Nilsson put forward a paradigm of AI based Sufficiency implies finding a guide to investigate the on a declarative representation of knowledge with semantic case of human beings. I would like improve problem-solving performances succeeds only because to present some ideas and concepts stemming from current syntax mirrors semantics in the domains where the research in Genetic Epistemology (GE), initiated by Jean programs were applied. Piaget, there is then no need for any distinction between This interrogation is precisely the core of the Piagetian rules and metarules or knowledge-base and inference engines. The "epistemic program" should undergo by itself a GE is concerned with knowledge considered as a process, series of revisions of represeutations, and thus experiment [Piaget (1964)]. The obvious point of convergence different schemes of perceptions-or inference enginesas of AI and GE is precisely this concept of knowledge as a the mathematico-logical structure underlying the dynamic process.

In the past twenty years, much time, effort, and money has been expended on designing an unambiguous representation of natural language to make them accessible to computer processing, These efforts have centered around creating schemata designed to parallel logical relations with relations expressed by the syntax and semantics of natural languages, which are clearly cumbersome and ambiguous in their function as vehicles for the transmission of logical data. Understandably, there is a widespread belief that natural languages are unsuitable for the transmission of many ideas that artificial languages can render with great precision and mathematical rigor. But this dichotomy, which has served as a premise underlying much work in the areas of linguistics and artificial intelligence, is a false one. There is at least one language, Sanskrit, which for the duration of almost 1000 years was a living spoken language with a considerable literature of its own. Besides works of literary value, there was a long philosophical and grammatical tradition that has continued to exist with undiminished vigor until the present century. Among the accomplishments of the grammarians can be reckoned a method for paraphrasing Sanskrit in a manner that is identical not only in essence but in form with current work in Artificial Intelligence. This article demonstrates that a natural language can serve as an artificial language also, and that much work in AI has been reinventing a wheel millenia old. First, a typical Knowledge Representation Scheme (using Semantic Nets) will be laid out, followed by an outline of the method used by the ancient Indian grammarians to analyze sentences unambiguously. Finally, the clear parallelism between the two will be demonstrated, and the theoretical implications of this equivalence will be given.

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.

Cambridge, Mass.: MIT Press

Berkeley, Calif: University of California Press

In IJCAI-85, pp. 1002--1009.

Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, Calif., pp. 61-70