I agree with much of what Dr. Levinson has to say, especially

A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem.

The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, low-cost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognizes this problem but does not address it effectively. The work presented by this Ph.D. dissertation was motivated by the desire to generate good, cost-effective schedules in dynamic and stochastic manufacturing environments.

The central thesis of my dissertation (Kocabas 1989)1 is that in complex systems, descriptive and definitive knowledge can be organized into functional categories; this categorization provides clarity and efficiency in representation and facilitates the integrated use of various methods of learning. I describe a formalism for organizing knowledge into such functional categories and some of its implementations. In this formalism, descriptive scientific knowledge is classified into seven categories. The categorization formalism allows complex propositions to be analyzed into their simple constituents; in turn, these constituents can be maintained in their categories. They can then be combined using a simple transformation function to form complex constructs such as frames and schemata. The methodology facilitates the implementation of knowledge-level methods of learning such as similarity-based learning, explanation-based learning, and conceptual clustering. It simplifies the identification and resolution of conflicts in knowledge systems.

In some Winter, 1991) brought a broad nostalgic The principles of statistical pattern mature and highly technical disciplines, smile to my face. I believe that recognition I had employed then are this mode of thought can be I am the unnamed Yale junior faculty very general indeed. Unfortunately, this is not member to whose work Prof. Schank very principles form the basis of all the case in our chosen pursuit of the alluded. Perhaps the intervening contemporary speech recognition essence of mind which should be years have eradicated his memory of systems which, in their best incarnations seen as a young and interdisciplinary my name or, more likely, he wished here at Bell Laboratories and enterprise. I, transcribing fluent speech of virtually organisms, she was not constrained however, fully mindful of Oscar any speaker talking about a specific by the academic boundaries that Wilde's observation that the only topic and using a vocabulary of thousands have since evolved.

The Fredkin Challenge in AI Research and Applications at Chandrasekaran, B. see nlso Hendler, Match, 2(2): Summer 1981, 30-33.

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"We report results from large-scale experiments in satisfiability testing. As has been observed by others, testing the satisfiability of random formulas often appears surprisingly easy. Here we show that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which satisfiability testing is quite difficult. Our results provide a benchmark for the evaluation of satisfiability-testing procedures." Proc. AAAI-92.

"We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N-queens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a model-finding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks." Proc. AAAI-92.

The Neural Prediction Model is the speech recognition model based on pattern prediction by multilayer perceptrons. Its effectiveness was confirmed by the speaker-independent digit recognition experiments. This paper presents an improvement in the model and its application to large vocabulary speech recognition, based on subword units. The improvement involves an introduction of "backward prediction," which further improves the prediction accuracy of the original model with only "forward prediction". In application of the model to speaker-dependent large vocabulary speech recognition, the demi-syllable unit is used as a subword recognition unit.