Recent general AI conferences show a decline in both the number and the quality of vision papers, but there is tremendous growth in, and specialization of, computer vision conferences. Hence, one might conclude that computer vision is parting or has parted company with AI. This article proposes that the divorce of computer vision and AI suggested here is actually an open marriage: Although computer vision is developing through its own research agenda, there are many shared areas of interest, and many of the key goals, assumptions, and characteristics of computer vision are also clearly found in AI.

This paper analyzes the correctness of the subsumption algorithm used in CLASSIC, a description logic-based knowledge representation system that is being used in practical applications. In order to deal efficiently with individuals in CLASSIC descriptions, the developers have had to use an algorithm that is incomplete with respect to the standard, model-theoretic semantics for description logics. We provide a variant semantics for descriptions with respect to which the current implementation is complete, and which can be independently motivated. The soundness and completeness of the polynomial-time subsumption algorithm is established using description graphs, which are an abstracted version of the implementation structures used in CLASSIC, and are of independent interest.

Analogical although analogy can help, as note that although still in its infancy reasoning is thus achieved in such well as hamper, learning. The role of and somewhat simplistic in character, systems by mainly keeping the analogy in learning is discussed by connectionist research might prove abstract relational microfeatures. Ann Brown and by Rand Spiro et al., to have an edge in tackling these Rumelhart proposes another way and the role of analogy in knowledge problems. The research described in for achieving analogical reasoning, acquisition is discussed by Brian Ross this book presents a grand challenge that is, "soft clamp," in which input and by John Bransford et al.; Stella and a future prospect for AI clamps can be overridden, and the Vosniadou studies the developmental researchers (traditional or connectionistic) rule of thumb is that the more concrete change in the use of analogy. Because in their endeavor to find a a feature is, the easier it can be part 3 of the book is of marginal better and more cognitively plausible overridden. The system finds the interest to AI, I do not discuss it any representation scheme.

Terminological knowledge representation systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). We analyze from a theoretical point of view a TKRS whose capabilities go beyond the ones of presently available TKRSs. The new features studied, often required in practical applications, can be summarized in three main points. First, we consider a highly expressive terminological language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, we allow to express inclusion statements between general concepts, and terminological cycles as a particular case. Third, we prove the decidability of a number of desirable TKRS-deduction services (like satisfiability, subsumption and instance checking) through a sound, complete and terminating calculus for reasoning in ALCNR-knowledge bases. Our calculus extends the general technique of constraint systems. As a byproduct of the proof, we get also the result that inclusion statements in ALCNR can be simulated by terminological cycles, if descriptive semantics is adopted.

PROSE is a knowledge-based configurator platform for telecommunications products. Its outstanding feature is a product knowledge base written in C-classIC, a frame-based knowledge representation system in the KL-ONE family of languages. Unlike previous configurator applications, the PROSE knowledge base is in a purely declarative form that provides developers with the ability to add knowledge quickly and consistently. The PROSE architecture is general and is not tied to any specific telecommunications product.

PITCH EXPERT was developed to make expertise available to mill-site engineers to solve pitch problems in kraft pulp mills. These problems have been estimated to cause losses to the Canadian pulp and paper industry in excess of $80 million each year. The design of the system took into account not only the complexity of the process interactions and the need for accuracy and completeness of recommendations but also the ongoing need for training mill personnel and the requirement that the system be maintainable and expandable without the constant involvement of the developers. PITCH EXPERT is now accessible by modem, and the savings achieved through use of the system covered the development costs within six months of release.

Because of their occasional need to return to shallow points in a search tree, existing backtracking methods can sometimes erase meaningful progress toward solving a search problem. In this paper, we present a method by which backtrack points can be moved deeper in the search space, thereby avoiding this difficulty. The technique developed is a variant of dependency-directed backtracking that uses only polynomial space while still providing useful control information and retaining the completeness guarantees provided by earlier approaches.

Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have.

The Association for the Advancement of Artificial Intelligence held its 1992 Fall Symposium Series on October 23-25 at the Royal Sonesta Hotel in Cambridge, Massachusetts. This article contains summaries of the five symposia that were conducted: Applications of AI to Real-World Autonomous Mobile Robots, Design from Physical Principles, Intelligent Scientific Computation, Issues in Description Logics: Users Meet Developers, and Probabilistic Approaches to Natural Language.

Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.