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.

The Association for the Advancement of Artificial Intelligence held its 1992 Fall Symposium Series on October 23-25 at the Royal Sonesta Hotel in Cambridge, Massachusetts. This article contains summaries of the five symposia that were conducted: Applications of AI to Real-World Autonomous Mobile Robots, Design from Physical Principles, Intelligent Scientific Computation, Issues in Description Logics: Users Meet Developers, and Probabilistic Approaches to Natural Language.

This article informally examines the role of stored internal state (that is, memory) in the control of autonomous mobile robots. The difficulties associated with using stored internal state are reviewed. It is argued that the underlying cause of these problems is the implicit predictions contained within the state, and, therefore, many of the problems can be solved by taking care that the internal state contains information only about predictable aspects of the environment. This architecture was successfully used to control real-world and simulated real-world autonomous mobile robots performing complex navigation tasks.

My dissertation describes an approach to automatically formulating or selecting models of a target physical system for a given qualitative reasoning task. It was motivated by two observations regarding modeling in general and work in qualitative physics in particular. First, all model-based reasoning is only as good as the model used (Davis and Hamscher 1988). Second, no single model is adequate or appropriate for a wide range of tasks (Weld 1989).

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 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) largescale knowledge base management and access, (4) software configuration management, and (5) intrusion detection.

This article is a slightly modified version of an invited address that was given at the Eighth IEEE Conference on Artificial Intelligence for Applications in Monterey, California, on 2 March 1992. It describes the lessons learned in developing and implementing the Artificial Intelligence Research and Development Program at the National Aeronautics and Space Administration (NASA). These stages are similar to the "ages of artificial intelligence" that Pat Winston described a year before the NASA program was initiated. The final section of the article attempts to generalize some of the lessons learned during the first seven years of the NASA AI program into AI program management heuristics.

The Association for the Advancement of Artificial Intelligence held its 1992 Spring Symposium Series on March 25-27 at Stanford University, Stanford, California. This article contains a summary of the symposia that were conducted: Artificial Intelligence in Medicine, Cognitive Aspects of Knowledge Acquisition, Computational Considerations in Supporting Incremental Modification and Reuse, Knowledge Assimilation, Practical Approaches to Scheduling and Planning, Producing Cooperative Explanations, Propositional Knowledge Representation, Selective Perception, and Reasoning with Diagrammatic Representations.

Industrial managers, engineers, and technologists have many expectations from artificial intelligence and its application to knowledge-based systems. Although the past decade has witnessed a number of innovative applications of AI in manufacturing, the field is still in its infancy and holds even greater promise for the future. The AAAI Press book Artificial Intelligence Applications in Manufacturing, (from which the following article was selected) presents a number of articles that relate to the enhancement of planning and decision making capabilities in today's automated production environments.