Place he 1993 robot competition consisted of the Office, (2) Office Delivery, and (3) Lockheed Palo Alto Research Labs, Second Office Rearrangement. The unifying theme Place for these events was autonomous robotics in realistic office environments. The legs, and then to quickly complete a slalom office contained actual furniture, including course and recognize the finish wall. In the second event, Office Delivery, the This realistic environment was a hurdle for objective was to self-locate using an office conventional robotic sensory systems. Thinlegged map, search an area for a given object (a coffeepot), tables and chairs are nearly invisible to and then navigate to a specified sonars, as are black cabinets and bookcases to delivery area.

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.

Analogical although analogy can help, as note that although still in its infancy reasoning is thus achieved in such well as hamper, learning. The role of and somewhat simplistic in character, systems by mainly keeping the analogy in learning is discussed by connectionist research might prove abstract relational microfeatures. Ann Brown and by Rand Spiro et al., to have an edge in tackling these Rumelhart proposes another way and the role of analogy in knowledge problems. The research described in for achieving analogical reasoning, acquisition is discussed by Brian Ross this book presents a grand challenge that is, "soft clamp," in which input and by John Bransford et al.; Stella and a future prospect for AI clamps can be overridden, and the Vosniadou studies the developmental researchers (traditional or connectionistic) rule of thumb is that the more concrete change in the use of analogy. Because in their endeavor to find a a feature is, the easier it can be part 3 of the book is of marginal better and more cognitively plausible overridden. The system finds the interest to AI, I do not discuss it any representation scheme.

Although the economic results of PDEC's green engineering techniques are only beginning to come in, they are nonetheless compelling. In addition, as green engineering grows in practice, the outset for the entire life cycle of new jobs in remanufacturing have been applied to a product, designing for component and recycling will be created. The consortium is currently Common Lisp. It plots a cost curve A pioneering consortium at engaged in two major development that represents the effort put into Carnegie Mellon University (CMU) is activities: (1) green indicators that are disassembly, testing, repair and using AI, combined with operations measures of environmental compatibility remanufacturing, quality assurance, research, environmental science, and (2) tools that use the green and product design changes that public policy, and other disciplines, indicators to help designers make allow for recovery. It also plots a to build tools for green engineering.

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.

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. I was one of the developers of JACK, the second-place finisher in the Coffeepot event. This article contains my personal recollections of the lab and contest.

In his recent papers, entitled Intelligence without Representation and Intelligence without Reason, Brooks argues for mobile robots as the foundation of AI research. This article argues that even if we seek to investigate complete agents in real-world environments, robotics is neither necessary nor sufficient as a basis for 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. First, the cost, effort, and expertise necessary to develop and systematically experiment with software artifacts are relatively low. Second, software environments circumvent many thorny but peripheral research issues that are inescapable in physical environments. 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. For example, the UNIX softbot under development at the University of Washington has led us to investigate planning with incomplete information, interleaving planning and execution, and a host of related high-level issues.

Terminological knowledge representation systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). We analyze from a theoretical point of view a TKRS whose capabilities go beyond the ones of presently available TKRSs. The new features studied, often required in practical applications, can be summarized in three main points. First, we consider a highly expressive terminological language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, we allow to express inclusion statements between general concepts, and terminological cycles as a particular case. Third, we prove the decidability of a number of desirable TKRS-deduction services (like satisfiability, subsumption and instance checking) through a sound, complete and terminating calculus for reasoning in ALCNR-knowledge bases. Our calculus extends the general technique of constraint systems. As a byproduct of the proof, we get also the result that inclusion statements in ALCNR can be simulated by terminological cycles, if descriptive semantics is adopted.

To support the goal of allowing users to record and retrieve information, this paper describes an interactive note-taking system for pen-based computers with two distinctive features. First, it actively predicts what the user is going to write. Second, it automatically constructs a custom, button-box user interface on request. The system is an example of a learning-apprentice software- agent. A machine learning component characterizes the syntax and semantics of the user's information. A performance system uses this learned information to generate completion strings and construct a user interface. Description of Online Appendix: People like to record information. Doing this on paper is initially efficient, but lacks flexibility. Recording information on a computer is less efficient but more powerful. In our new note taking softwre, the user records information directly on a computer. Behind the interface, an agent acts for the user. To help, it provides defaults and constructs a custom user interface. The demonstration is a QuickTime movie of the note taking agent in action. The file is a binhexed self-extracting archive. Macintosh utilities for binhex are available from mac.archive.umich.edu. QuickTime is available from ftp.apple.com in the dts/mac/sys.soft/quicktime.