Dartnall, Terry, see Kim, Steven Davis, Randall; Shrobe, Howard; and Szolovits, Peter. What Is a Knowledge 1992 AAAI Robot Exhibition and Competition Leonard, Lisa. Dean, Thomas; and Bonasso, R. Capture and Use, The, see Lee, Jintae Technologies, see Barachini, Franz Cannel Versus Flakey: A Comparison of Dean, Tom, see Joskowicz, Leo. Reasoning Dorr, Bonnie J. Building Lexicons for see Tanner, Steve with Diagrammatic Representations: A Machine Translation: 1993 Spring Anick, Peter; and Simoudis, Evange-Report on the Spring Symposium. Retrieval: 1993 Spring Symposium Charniak, Eugene, see Goldman, Drummond, Mark, see Lansky, Amy Report.

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.

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.

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.

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.

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.

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.

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.

My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.

My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.