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 International Workshop on Processing Declarative Knowledge was held in Kaiserslautern, Germany, from 1 to 3 July 1991. The workshop was intended as a forum for the presentation of new approaches to processing declarative knowledge, the discussion of procedural versus alternative paradigms, and the issues concerned with efficient processing of realistic knowledge bases. Demonstrations of implemented systems were also announced.

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

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.

Integrated Language and Vision Systems, Scholarship Travel Program If you are interested in assisting AAAI at the national conference, New Mexico State University, Continued please contact AAAI at volunteer Dec. 1991 AAAI announces the continuation of @aaai.org. All inquiries should 1991 IFIP/KR Workshop its scholarship travel program for students include your name, address, telephone, Eleventh International Workshop on who want to attend the National advisor's name, and email Distributed Artificial Intelligence, Conference on Artificial Intelligence address. All requests to volunteer at Glen Arbor, Michigan, February 1992 in San Jose, California, 12-17 July AAAI-92 must be received by the 1992. First International Conference on and (2) are members of April 3 AAAI-92 Scholarship AI Planning Systems, University of AAAI. In addition, repeat scholarship Application Deadline Maryland, June 1992 applicants must have fulfilled the April 29 Al Magazine Summer Issue The Third International Conference volunteer and reporting requirements Calendar Deadline on Principles of Knowledge Representation for previous awards.

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 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 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.