The American Association for Artificial Intelligence sponsored a number of workshops in conjunction with the Eleventh National Conference on Artificial Intelligence held 11-15 July 1993 in Washington, D.C. This article contains reports of four of the workshops that were conducted: AI Models for System Engineering, Case-Based Reasoning, Reasoning about Function, and Validation and Verification of Knowledge-Based Systems. This article contains reports of four of the workshops that were conducted: AI Models for System Engineering, Case-Based Reasoning, Reasoning about Function, and Validation and Verification of Knowledge-Based Systems. The AI Models for System Engineering Workshop included 11 presentations divided into 2 broad categories: (1) the need for using AI in system engineering and (2) existing AI applications in system engineering. A morning discussion centered on large system engineering problems that could benefit from AI: modelbased system engineering, the monitoring of the effects of change in large systems, organizational aspects of system engineering, and the integration of software into large systems.

This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Such information is commonly exchanged between a sales assistant and a customer during in-store recommendation processes and is usually termed an explanation (Brewer, Chinn, and Samarapungavan 1998). We define explanations in recommender systems by two properties. First, they are information about recommendations, where a recommendation is typically a ranked list of items.

Many of the problems we encounter in our day-to-day lives involve deciding between a set of options, or candidates. In these kinds of problems, one needs to determine the worthiness of each candidate to select the best one. Other problems involve assessing an individual candidate's strengths and weaknesses to suggest ways of improving its performance. Both types of problems require the process of evaluation. In schools, for example, students are evaluated for both remedial purposes and selection and ranking.

The Digital Services Research Group and its predecessor groups and offshoots in Digital Equipment Corporation have been mobilizing leading-edge AI research to bear on real-life problems that face the corporation and its customers. The general strategy of the group is to explore emerging techniques relevant to service and support needs through developing rapid prototypes, deploying these prototypes, and incorporating feedback from users. With over 32 major projects undertaken during the past decade, we have worked on a broad spectrum of problems and explored a variety of advanced AI techniques. This article describes the current AI activities in five areas: (1) enterprise advisory systems, (2) natural language processing and textual information retrieval, (3) large-scale knowledge base management and access, (4) software configuration management, and (5) intrusion detection. We also list some future research directions.

It is generally accepted that knowledge has a contextual component. However, even if its importance is acknowledged, this contextual component is rarely represented explicitly in available knowledge representation systems and is not used in subsequent processing of knowledge. Thus, there is a gap between what is known and what is done. Acquisition, representation, and exploitation of knowledge in context would have a major contribution in knowledge representation, knowledge acquisition, explanation, maintenance, documentation, learning, human-computer communication, and validation or verification. A computational capability to understand, represent, and reason about context will be valuable for, and of immense benefit to, many AI problems.

Czerwinski, Mary, see Nguyen, Trung 1992 AAAI Robot Exhibition and Competition see Dean, Thomas 1992 Workshop on Design Rationale Capture and Use, The, see Lee, Jintae Advances in Real-Time Expert System Technologies, see Barachini, Franz AI and Creativity: 1993 Spring Symposium Report, see Kim, Steven AI and N&Hard Problems: 1993 Spring Symposium Report, see Crawford, James AI Research and Application Development at Boeing's Huntsville Laboratories see Tanner, Steve Anick, Peter; and Simoudis, Evange-10s. Agent Architectures, see Hanks, Steve Berman, Jay I. see Wright, Jon R. Bonasso, R. Peter see Dean, Thomas Bookman, Lawrence, see Sun, Ron Brown, Karen E. see Wright, Jon R. Building Lexicons Two Winner see Congdon, Clare Carnes, Ray, see Tanner, Steve Case-Based Reasoning and Information Retrieval: 1993 Spring Symposium Report, see Anick, Peter Chandrasekaran, B.; Narayanan, N. Hari; and Iwasaki, Yumi. Charniak, Eugene, see Goldman, Robert l? Chien, Steve, see Gat, Erann. Cohen, Paul R., see Hanks, Steve Compaq Quicksource: Providing the Consumer with the Power Drummond, Mark, see Lansky, Amy Engineering Design through Constraint-Based Reasoning, see Murtagh, Niall Etzioni, Oren. Goal-Driven Learning: Fundamental Issues: A Symposium Report, see Leake, David Goldman, Robert l?; Charniak, Eugene; Gale, William; and Norvig, Peter.

Barbara Grosz and Randall Davis will take over leadership of the American Association for Artificial Intelligence for the next two years. Grosz, Gordon McKay Professor of Computer Science at Harvard, is the new President, succeeding Patrick Hayes of the University of Illinois. Randall Davis of MIT is President-Elect. "In the last several years," Grosz said, "AI has played an increasingly central role in the development of systems for effective management of complex information and flexible access to large knowledge bases. At the same time, there have been significant advances across a spectrum of AI subfields. "The employment of AI techniques will become even more essential in the future," Grosz pointed out, "as computer systems allow access to increasing amounts of diverse types of information that will become available in electronic form. AAAI will continue to provide forums for presenting advances in AI science and technology, and to encourage interaction between the business and ...

Hojjat Adeli, coauthor of "A Novel Approach to Expert Systems for the Design of Large Structures, " is currently a professor of civil engineering at The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210. He received his Ph.D. from Stanford University in 1976 and is editor-inchief of the International Journal of Microcomputers in Civil Engineering. A contributor to 20 journals, he is the author or editor of over 160 publications in various fields of computer-aided engineering and is the editor of the forthcoming book series Knowledge Engineering, to be published by McGraw-Hill The first two volumes are scheduled for publication in mid-1989. Dean Allemang, coauthor of "Connectionism and Information Processing Abstractions: The Message Still Counts More Than the Medium," is a graduate research fellow at the Laboratory for Artificial Intelligence Research in the Department of Computer and Information Science at The Ohio State University, Columbus, Ohio 43210. He is currently writing his Ph.D. dissertation on a devicebased understanding of software.

James Peters, coauthor of "A Knowledge-Based Model of Audit Risk," is an assistant professor in the Department of Accounting, College of Business Administration, University of Oregon. Hans Berliner, author of the Hitech Computer Chess report, is a senior scientist in the Department of Computer Science, Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213. R. Peter Bonasso, author of "An Assessment of What AI Can Do for Battle Management--A Report of the First AAAI Workshop on AI Applications to Battle Management" is the department head of the Artificial Intelligence Technical Center in The MITRE Corporation, Washington 01 Operations division, 7525 Colshire Drive, Mclean, VA 22102. His research interests include commonsense reasoning and qualitative processes with a view toward applications to military systems. Vasant Dhar, coauthor of "A Knowledge-Based Model of Audit Risk," is an associate professor in the Department of Information Systems, New York University.

AAAI announces the continuation of its scholarship travel program for students who want to attend the National Conference on Artificial Intelligence in San Jose, California, 12-17 July 1992. Undergraduate or graduate students enrolled in a full-time degree program at any college or university are eligible to serve as student volunteers during AAAI-92, to be held at the San Jose Convention Center in San Jose, California, 12-17 July. In exchange for assisting AAAI staff members during your volunteer shift, you will receive complimentary conference registration, a copy of the AAAI-92 proceedings, and a special AAAI-92 T-shirt. If you are interested in assisting AAAI at the national conference, please contact AAAI at volunteer @aaai.org. All inquiries should include your name, address, telephone, advisor's name, and email address.