My Ph.D. thesis (Ward 1992, 1991)1 addressed the task of generating natural language utterances. Current generators only accept input that are relatively poor in information, such as feature structures or lists of propositions; they are unable to deal with input rich in information, as one might expect from, for example, an expert system with a complete model of its domain or a natural language understander with good inference ability. FIG is based on a single associative network that encodes lexical knowledge, syntactic knowledge, and world knowledge. Thus, FIG is a spreading activation or structured connectionist system (Feldman et al.

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.

The Association for the Advancement of Artificial Intelligence held its 1991 Fall Symposium Series on November 15-17 at the Asilomar Conference Center, Pacific Grove, California. This article contains summaries of the four symposia: Discourse Structure in Natural Language Understanding and Generation, Knowledge and Action at Social and Organizational Levels, Principles of Hybrid Reasoning, Sensory Aspects of Robotic Intelligence.

The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems.

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 workshop "Advances in Interfacing Production Systems with the Real World" was designed to bring together researchers from around the world to focus on the problem of integrating production systems into industrial environments. It was held on 25 August 1991 in Sydney, Australia, in conjunction with the Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91). Nine papers were accepted for the proceedings, and six of them were discussed at the workshop.

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.

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. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques.

The Executive Council may change the principal office in California The name of this corporation shall be the American Association from one location to another. The corporation may have such other offices, either within or without the State of California, ARTICLE II. This corporation is a nonprofit public benefit corporation and is not organized for the private gain of any person. MEMBERS is organized under the California Nonprofit Corporation Law for scientific and educational purposes in the field of Section 1. Classes and Privileges. Student members have all the rights and privileges of Regular ARTICLE III. The Executive Council shall determine (a) This corporation is organized and operated exclusively the qualifications for membership in the corporation.

Human error is an Criticism should not be querulous, and umes of fast-changing increasingly important wasting, all knife and root puller, but guiding, sensory data that and addressable instructive, inspiring, a South wind, one needs to process concern in modernday not an East wind. Most institutions), and the automation that technology represents accidents waiting to surrounds us (for example, unfriendly computers happen. For example, in the Challenger explosion, We get by because humans excel at coping. the shortcomings of the O-rings had been High-technology accidents occur because known for several years. What feedback hundreds of alarms simultaneously all contributed strategy (for example, story telling, first-principle to the disaster. Likewise, when the lecturing) will most constructively correct British fleet was sent to defend the Falkland the human error? It was at this differences. However, there are no point that the Argentines released their missile models there or in the AI literature of errors and sank an unsuspecting British ship. The operator had The errors result from proficient task performers no inkling of the ramifications of the system practicing in a natural environment; they designs under the current operating conditions. New error and critiquing models operator has virtually no way out. The remarkable need to capture and reflect this difference. We computer-aided design (ICAD) to mitigate begin by examining the design process and such problems. Specifically, we examine the the cognitive difficulties it poses. The designer uses a interference problems are also increasingly variety of cognitive operators to generate a evident on civilian automobiles, airplanes, design, test it under various conditions, refine and ships that cram telephones, radios, computers, it until a stopping rule is reached, and then radar devices, and other electromagnetically store the design as a prototype or analog to incompatible devices into close help start a new process for the next design proximity. The design process is sufficiently complex domain are relevant to all engineering design that a correct and complete design applications that must factor any operational simply cannot be deduced from starting conditions (or manufacturability, sales, or other downstream) or simulation model results.