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.

Dynamic Constraints," present a images or pictures because "having Gregory shows how it can be made

Alan Turing's decades-old question still influences artificial intelligence because of the simple test he proposed in his article in Mind. In this article, AI Magazine collects presentations about the first round of the classic Turing Test of machine intelligence, held November 8, 1991 at The Computer Museum, Boston. Robert Epstein, Director Emeritus, Cambridge Center for Behavioral Studies, and an adjunct professor of psychology, Boston University, University of Massachusetts (Amherst), and University of California (San Diego) summarizes some of the difficult issues during the planning of this first real-time competition, and describes the event. Presented in tandem with Dr. Epstein's article is the actual transcript of session that won the Loebner Prize Competition--Joseph Weintraub's computer program PC Therapist. In 1985 an old friend, Hugh Loebner, told me The intricacies of setting up a real Turing Test excitedly that the Turing Test should be made that would ultimately yield a legitimate into an annual contest. We were ambling winner were enormous. Small points were down a Manhattan street on our way to occasionally debated for months without dinner, as I recall. Hugh was always full of clear resolution. Turing, proposed a variation on a simple Four years later, while serving as the director parlor game as a means for identifying a of the Cambridge Center for Behavioral Studies, machine that can think: A human judge an advanced studies institute in Massachusetts, interacts with two computer terminals, one I established the Loebner Prize controlled by a computer and the other by a Competition, the first serious effort to locate person, but the judge doesn't know which is a machine that can pass the Turing Test. If, after a prolonged conversation at Hugh had come through with a pledge of each terminal, the judge can't tell the difference, $100,000 for the prize money, along with we'd have to say, asserted Turing, that some additional funds from his company, in some sense the computer is thinking. Crown Industries, to help with expenses. The Computers barely existed in Turing's day, but, quest for the thinking computer had begun. I'll then describe that After much debate, the Loebner Prize Committee first event, which took place on November 8, ultimately rejected Turing's simple 1991, at The Computer Museum in Boston two-terminal design in favor of one that is and offer a summary of some of the data generated more discriminating and less problematic. Finally, I'll speculate The two-terminal design is troublesome for about the future of the competition--now an several reasons, among them: The design presumes annual event, as Hugh envisioned--and that the hidden human--the human about its significance to the AI community.

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

Dynamic Constraints," present a images or pictures because "having Gregory shows how it can be made

The Department of Computer Science at Louisiana State University (LSU) has been involved in robotics research since 1992 when the Robotics Research Laboratory (RRL) was established as a research and teaching program specializing in autonomous mobile robots (AMRS). Researchers at RRL are conducting high-quality research in amrs with the goal of identifying the computational problems and the types of knowledge that are fundamental to the design and implementation of autonomous mobile robotic systems. In this article, we overview the projects that are currently under way at LSU's RRL.

I agree with much of what Dr. Levinson has to say, especially

A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem.

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 central thesis of my dissertation (Kocabas 1989)1 is that in complex systems, descriptive and definitive knowledge can be organized into functional categories; this categorization provides clarity and efficiency in representation and facilitates the integrated use of various methods of learning. I describe a formalism for organizing knowledge into such functional categories and some of its implementations. In this formalism, descriptive scientific knowledge is classified into seven categories. The categorization formalism allows complex propositions to be analyzed into their simple constituents; in turn, these constituents can be maintained in their categories. They can then be combined using a simple transformation function to form complex constructs such as frames and schemata. The methodology facilitates the implementation of knowledge-level methods of learning such as similarity-based learning, explanation-based learning, and conceptual clustering. It simplifies the identification and resolution of conflicts in knowledge systems.