Search algorithms for finding optimal solutions are, at least from the practical point of view, often enough intractible, so that the search for good ('satisficing') solutions becomes a research topic of its own interest. Satisficing solutions and different approaches to obtain them under various criteria is the subject of these notes, published in the series "Research notes in artificial intelligence". In an introductory chapter the author presents the known point - value and the point - { { set of values} } identification used in search- based decision-algorithms for guiding the search and discusses some of their advantages and disadvantages. This motivates the here studied alternative approach using that evaluation functions do not return a point - value or a range of values corresponding to a point (state) in a tree but now a distribution function, that describes the possible location of the'value' of the state. Chapter 2 introduces this model, Chapter 6 resumes the basic results.

Letters to the Editor

Canadians have made many contributions to artificial intelligence over the years. This article presents a summary of current research in artificial intelligence in Canada and acquaints readers with the Canadian organization for artificial intelligence -- the Canadian Society for the Computational Studies of Intelligence / Societe Canadienne pour l' Etude de l'Intelligence par Ordinateur (CSCSI/ SCEIO).

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. 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.

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.

Subjects include AI's impact on employment, the AAAI conference, a response to McCarthy's Presidential Message, AI going public, and computerless expert systems.

Canadians have made many contributions to artificial intelligence over the years. This article presents a summary of current research in artificial intelligence in Canada and acquaints readers with the Canadian organization for artificial intelligence -- the Canadian Society for the Computational Studies of Intelligence / Societe Canadienne pour l' Etude de l'Intelligence par Ordinateur (CSCSI/ SCEIO).

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.

It may be argued that this, in principle, is a more realistic approach because it addresses, rather than finesses, the problem of incomplete information in the knowledge base. On the other hand, the Dempster-Shafer theory provides a basis-at least at present-for only a small subset of the rules of combination which are needed for inferencing in expert systems. In particular, the theory does not address the issue of chaining, nor does it come to grips with the fuzziness of probabilities and certainty factors. Thus, although the theory is certainly a step in the right direction, for it provides a framework for dealing with granular data, it does require a great deal of further development to become a broadly useful tool for the management of uncertainty in expert systems. Although not easy to understand, Shafer's book contains a wealth of significant results, and is a must for anyone who wants to do serious research on problems relating to the rules of combination of evidence in expert systems. Indeed, there is no doubt that, in the years to come, the Dempster-Shafer theory and its extensions will become an integral part of the theory of such systems and will certainly occupy an important place in knowledge engineering and related fields.

I suspect that their motive was summary 3. the agent can justify its belief that s is true, i e., rejection of newfangled techniques, not eymological In any case I answered them. Perhaps your readers may be interested in the knows s]). An agent's problem-solving behavior is intelligent if (and to the extent that) the agent's problemsolving Sincerely yours, An agent's problem-solving behavior is artificially intelligent if the behavior is intelligent and the Definition 1. An agent lcnows some statements if the agent is a machine. Remark: It is a consequence of this definition of artificial 1. s is true, i.e, s is either a logical truth (a theorem intelligence that artificial intelligence does not equal artificial or a tautology) or a factual truth (a correspondence endocrinology! with fact);