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.

A robot-building lab and contest was held at the Eleventh National Conference on Artificial Intelligence. Teams of three worked day and night for 72 hours to build tabletop autonomous robots of legos, a small microcontroller board, and sensors. The robots then competed head to head in two events. This article contains my personal recollections of the lab and contest.

In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning.

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.

The second annual Robot Competition and Exhibition sponsored by the Association for the Advancement of Artificial Intelligence was held in Washington D.C. on 13-15 July 1993 in conjunction with the Eleventh National Conference on Artificial Intelligence. This article describes the robots that placed first and second in each event and compares their strategies and their resulting successes and difficulties.

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.

This project has entered into a joint agreement agreements with Nestor Inc. (Providence, will involve simulating fires in buildings with software giant Microsoft Corp. R.I.) to institute pilot projects and assessing how a VR system as the first step toward installing (Redmond, Wash.) that will enable Planet is an expert system that individual cardholders and banks provides strategic and detailed planning against losses due to fraud, making for financial audits. It produces Bank Bandeirantes (Sao Paulo, Brazil) use of neural networks to learn a cardholder's risk assessments for a variety of financial has teamed up with another Brazilian pattern of credit card use. Venus is a flowcharting tool analysis expert system that operates in Cal.) will team up with VRl Entertainment for auditors who specialize in auditing real-time. The intelligent system has Inc. (Boulder, Colo.) to computer systems running deliver virtual reality to the home via helped the bank experience dramatic SIRIUS (SWIFT's Intelligent examines in detail a P 1 the key AI technologies: telecommunications operators Contact Lionheart Publishing system supervises the 150 switches Inc,2555 Cumberland Parkway, an event manager and hypertext Suite 299, Atlanta, GA 30339, (404) 434-manuals, and fields hundreds of telephone and 350 connections that make up 2187, FAX: (404) 432-6969. Wash.) have teamed up to develop Intelligent Alarming, which integrates Metropolitan Federal Bank (Edina, Mitek Systems (San Diego, Cal.) has industrial automation software Minn.) has deployed an automated completed a test phase for an automatic with an expert system.

The methodological underpinnings of AI are slowly changing. Benchmarks, test beds, and controlled experimentation are becoming more common. Although we are optimistic that this change can solidify the science of AI, we also recognize a set of difficult issues concerning the appropriate use of this methodology. 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.