AAAI Workshop on Cooperation Among Carifio, Mike see Rewari, Anil. Language, A, 13(1): Spring 1992, 9-Chalupsky, Hans see Shapiro, Stuart 13(2): Summer 1992, 39-42. Fourth International Symposium on Chen, Tony see Hadavi, Khosrow. Christopher J. Knowledge Discovery Adler, Mark see Rewari, Anil. Functional Categorization of Knowledge: Downes, Stephen see Dietrich, Eric.

NASA's AI program has implemented Rather, it is to attempt to describe the lessons learned in the process of putting the program in setting up and carrying out the first together and carrying it out. Research and Development Program at the Did the plan work? How did National Aeronautics and Space Administration the program readjust? This AI program is sponsored by faced, and how would they be handled differently NASA's Office of Aeronautics and Space Technology. What are the heuristics used to The program conducts research and keep NASA's AI ship afloat in the churning development at the NASA centers (Ames, seas of government politics? It team never got lost in the process of setting also sponsors research in academia and industry, up the AI program, there were a few times primarily through Ames Research Center, when it was temporarily directionally disoriented. There were encounters with the NASA. The AI group at Ames, which is headed unforeseen that called for real-time reactive by Peter Friedland, has particular strengths in replanning.

The interdisciplinary makeup allowed for an expansion of the scope of Glymour's One notable extension was the move from android epistemology to android ethics. "they can know everything we know Margaret Boden presented her work Hayes and Ford were responding Participation was limited to 40 If the first two workshops on to the debate in Scientific American researchers selected from several disciplines human and machine cognition are (January 1990) between Searle and (principally computer science, representative, these meetings will the Churchlands about whether a philosophy, and psychology); become hotbeds of constructive and machine could think. Ironically, although this approach makes for much-needed debate. They focus on from the perspective of Hayes and stimulating discussion, it has resulted the foundational and methodological Ford, Searle and the Churchlands are in a competitive review process concerns of those who want to forge essentially in agreement, diverging (about a 10-percent acceptance rate). It is just a theories about the necessary in U.S. politics, the theme of the fact of life that there isn't much material basis (biological versus parallel) Second International Workshop on agreement about methodology and for intelligence. They both Human and Machine Cognition was, foundational issues within these two make specific implementation features What do androids know, and when fields. The positions covered One feature of the workshop that for intelligence. As might be expected, a wide range: "They can know facilitated and, at times, obstructed Paul Churchland objected to this only what androids can know: Android fruitful discussion was its highly interdisciplinary grouping.

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.

Research on planning for robots is in such a state of flux that there is disagreement about what planning is and whether it is necessary. We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. More research is needed on formal semantics for robot plans. However, we are already beginning to see how to mesh plan execution with plan generation and learning.

An effective to develop an abstract UI model which solution to this problem must have three encompasses the essence of those systems.

The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, low-cost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognizes this problem but does not address it effectively. The work presented by this Ph.D. dissertation was motivated by the desire to generate good, cost-effective schedules in dynamic and stochastic manufacturing environments.

The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, low-cost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognizes this problem but does not address it effectively. The work presented by this Ph.D. dissertation was motivated by the desire to generate good, cost-effective schedules in dynamic and stochastic manufacturing environments.

We argue that the problem of plan recognition, inferring an agent's plan from observations, is largely a problem of inference under conditions of uncertainty. We present an approach to the plan recognition problem that is based on Bayesian probability theory. In attempting to solve a plan recognition problem we first retrieve candidate explanations. These explanations (sometimes only the most promising ones) are assembled into a plan recognition Bayesian network, which is a representation of a probability distribution over the set of possible explanations. We perform Bayesian updating to choose the most likely interpretation for the set of observed actions.

Technical Report RL-TR-92-217, Rome Laboratory, Air Force Systems Command , Griffiss Air Force Base, NY 13441 -5700