The Objects and Artificial Intelligence Workshop was held on 25 August 1991 in conjunction with the 1991 International Joint Conference on Artificial Intelligence. The workshop brought together researchers in AI and object-oriented programming to exchange ideas and investigate possible avenues of cooperation between AI and object-oriented programming. The workshop dealt with both the theoretical and the practical aspects of this cooperation.

The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?

Immediately preceding the 1993 National Conference on Artificial Intelligence (NCAI) in Washington D.C., a new conference series on the application of AI to molecular biology was inaugurated in neighboring Bethesda, Maryland. The First International Conference on Intelligent Systems for Molecular Biology (ISMB-93), held 6-9 July 1993 at the Lister Hill Center of the National Library of Medicine (NLM), attracted over 200 computer scientists and biologists from 13 countries.

The Association for the Advancement of 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.

Similarity in Cognition: A Review of Similarity and Analogical Reasoning

Benchmarks, test beds, and controlled experimentation are becoming more common. We discuss these issues as they relate to research on agent design. We survey existing test beds for agents and argue for appropriate caution in their use. We end with a debate on the proper role of experimental methodology in the design and validation of planning agents.

For over a decade now, AI techniques have been applied to some of the hardest problems faced by business today, often with stellar results and a tenfold-plus return on investment. One of the major problems faced by businesses in the 1990s is how to produce environmentally friendly products and stay profitable. A pioneering consortium at Carnegie Mellon University (CMU) is using AI, combined with operations research, environmental science, public policy, and other disciplines, to build tools for green engineering. Green engineering is an approach to product development that balances environmental compatibility against economic profitability.

My Ph.D. dissertation develops and implements a new model of multiagent coordination, called JOINT RESPONSIBILITY (Jennings 1992b), based on the notion of joint intentions. The responsibility framework was devised specifically for coordinating behavior in complex, unpredictable, and dynamic environments, such as industrial control. The need for such a principled model became apparent during the development and the application of a general-purpose cooperation framework (GRATE) to two real-world industrial applications.

Celite corporation and Andersen Consulting have developed an advanced approach to traditional software development called the application software factory (ASF)." The approach is an integration of technology and total quality "management" techniques that includes the use of an expert system to guide module design and perform "module programming." The expert system component is called the knowledge-based design assistant and its inclusion in the ASF methodology" has significantly reduced module development time, training time, and module and communication errors.

In his recent papers, entitled Intelligence without Representation and Intelligence without Reason, Brooks argues for mobile robots as the foundation of AI research. The article proposes real-world software environments, such as operating systems or databases, as a complementary substrate for intelligent-agent research and considers the relative advantages of software environments as test beds for AI. Brooks's mobile robots tug AI toward a bottom-up focus in which the mechanics of perception and mobility mingle inextricably with or even supersede core AI research. In contrast, the softbots (software robots) I advocate facilitate the study of classical AI problems in real-world (albeit, software) domains.