Invariant Object Recognition Using a Distributed Associative Memory Harry Wechsler and George Lee Zimmerman Department or Electrical Engineering University or Minnesota Minneapolis, MN 55455 Abstract This paper describes an approach to 2-dimensional object recognition. Complex-log conformal mappingis combined with a distributed associative memory to create a system which recognizes objects regardless of changes in rotation or scale. Recalled information from the memorized database is used to classify an object, reconstruct the memorized version ofthe object, and estimate the magnitude of changes in scale or rotation. The system response is resistant to moderate amounts of noise and occlusion. Several experiments, using real,gray scale images, are presented to show the feasibility of our approach. Introduction The challenge of the visual recognition problem stems from the fact that the projection of an object onto an image can be confounded by several dimensions of variability such as uncertain perspective, changing orientation and scale, sensor noise, occlusion, and nonuniform illumination.

Nigel Ford's book purports to be both an introduction to AI and an examination of whether machines are cognizant entities.With this pairing, Ford intends to begin at the beginning, answering the question "what is AI?" and to proceed to his main thesis about whether machines can think. Unfortunately, Ford is unable to move on to the higher plane of his main thesis.

Yoav Shoham's revised doctoral dissertation is not fully comprehensible to all readers, but it provides a good introduction to reasoning about change; the references are sometimes incomplete, however.

Evaluation should be a mechanism of progress both within and across AI research projects. For the individual, evaluation can tell us how and why our methods and programs work and, so, tell us how our research should proceed. For the community, evaluation expedites the understanding of available methods and, so, their integration into further research. In this article, we present a five-stage model of AI research and describe guidelines for evaluation that are appropriate for each stage. These guidelines, in the form of evaluation criteria and techniques, suggest how to perform evaluation. We conclude with a set of recommendations that suggest how to encourage the evaluation of AI research.

Emphasizing theory and implementation issues more than specific applications and Prolog programming techniques, this book by David Maier and Davis S. Warren, respected researchers in logic programming, is a superb book.

He is a and Information Science at The and Information Science at The Distinguished Professor of Computer Ohio State University, Columbus, Ohio State University, Columbus, Science at the University of Minnesota, Ohio 43210. He is currently writing Ohio 43210. He is currently writing a position he has held since 1984.

The Fourth Uncertainty in Artificial Intelligence workshop was held 19-21 August 1988. The workshop featured significant developments in application of theories of representation and reasoning under uncertainty. A recurring idea at the workshop was the need to examine uncertainty calculi in the context of choosing representation, inference, and control methodologies. The effectiveness of these choices in AI systems tends to be best considered in terms of specific problem areas. These areas include automated planning, temporal reasoning, computer vision, medical diagnosis, fault detection, text analysis, distributed systems, and behavior of nonlinear systems. Influence diagrams are emerging as a unifying representation, enabling tool development. Interest and results in uncertainty in AI are growing beyond the capacity of a workshop format.

A novel approach is presented for the development of expert systems for structural design problems. This approach differs from the conventional expert systems in two fundamental respects. First, mathematical optimization is introduced into the design process. Second, a computer is used to obtain parts of the knowledge necessary in the expert systems in addition to heuristics and experiential knowledge obtained from documented materials and human experts. As an example of this approach, a prototype coupled expert system, the bridge truss expert (BTExpert), is presented for optimum design of bridge trusses subjected to moving loads. BTExpert was developed by interfacing an interactive optimization program developed in Fortran 77 to an expert system shell developed in Pascal. This new generation of expert systems-embracing various advanced technologies such as AI (machine intelligence), the numeric optimization technique, and interactive computer graphics -- should find enormous practical implications.

Second, the problem domain of the used be as good as possible. Two The application task requires little task is stable. This means that the characteristics of the domain expert or no common sense. Although domain should be well established can help determine the degree of researchers are continuing to study and unlikely to undergo vast changes expertise. First, the expert is highly the representation of commonsense during the life of the expert system respected by experienced people in the knowledge, no practical systems have project. This stability does not require domain field. Because the goal of the been developed to date (Lenat, that the problem-solving process project is often to simulate the Prakash, and Shepherd 1986). A problem required to perform the task be well expert's performance, this expert requiring common sense on the understood, simply that the basics of should be viewed by others as a genuine part of the expert should be left to a the task domain be established.

Is it because they have been "mastered," or have become "trivial?" Indeed, many new problems for these applications add to the complexity of the solutions rater than trivialize them. Indeed, the work of myself and course, is no longer mathematics. The YES/L1 language is an integration of artificial intelligence is at a of procedural and rule-based techniques. Condition-action rules in plateau from which it can rise or fall YES/L1 are driven by a RETElike algorithm.