What I like to see is more international volumes with a balanced set of multicultural views from the United States, Europe, and Asia. This goes back to Roy D'Andrade's However, a number of To my mind, some of the chapters to answer the question, "What is cognitive issues are repeated across chapters, and indulge in lots of talk without any science?" The book does answer it is not clear that the authors of each clear detail or data. I found that the question, in so far as it can in such chapter had a chance to read the other Daniel Schacter's chapter on memory a young field, by providing a range of chapters while they wrote theirs. The was too full of references to other chapters tackling cognitive science different parts of the book could have work and had little of his own discussion; from different points of view.

Research on constraints and agents is emerging at the intersection of the communities studying constraint computation and software agents. Constraint- based reasoning systems can be enhanced by using agents with multiple problem-solving approaches or diverse problem representations. The constraint computation paradigm can be used to model agent consultation, cooperation, and competition. An interesting theme in agent interaction, which is studied here in constraint-based terms, is confronting ignorance: the agent's own ignorance or its ignorance of other agents.

The Eleventh International Workshop on Qualitative Reasoning was held in Cortona, Italy, on 3 to 6 June 1997. Participants included scientists from both qualitative reasoning and quantitative mathematical modeling communities. This article summarizes the significant issues and discussion raised during the workshop.

One of the most common mechanisms used for speeding up problem solvers is macro-learning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macro-learning presents is the vast number of macros that are available for acquisition. Macros increase the branching factor of the search space and can severely degrade problem-solving efficiency. To make macro learning useful, a program must be selective in acquiring and utilizing macros. This paper describes a general method for selective acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of NxN sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.

Deneb Robotics (Auburn Hills, Mich.) has been awarded a $2.3 million contract from the National Institute of Standards and Technology (NIST) to develop the agent network for task scheduling and execution. This intelligent agent-based project is designed to improve existing factory-scheduling systems with a new task scheduling and execution system in which Shell U.K. Exploration and Production availability and prevent cars from agents represent factory resources, systems, (Aberdeen, U.K.) has implemented being damaged while they are parked. The Arvin Industries (Columbus, Ind.) is Cisco Systems (San Jose, Calif.), a supplier expert system helped Shell achieve working with the U.S. Air Force to of network technology, is using over $1.6 million in cost savings for develop a neural network system that intelligent-agent technology to integrate its Brent Field site within 2 months of can determine the quality of noise in CD-ROM and online web information implementation. The neural network will help The addition of intelligent The National Research Council has determine what exactly an annoying search-and-retrieval capabilities has awarded Nestor (Providence, R.I.) a sound is and how it can be fixed. Mercedes-Benz plans This system has helped cut specialty Neural Computer Sciences (NCS) to establish three vrf test sites in clinic costs by 40 percent.

To effectively generate a fast and consistent apartment construction project network, Hyundai Engineering and Construction and Korea Advanced Institute of Science and Technology developed a case- and constraint-based project-planning expert system for an apartment domain. The system, FAS-TRAK- APT, is inspired by the use of previous cases by a human expert project planner for planning a new project and the modification of these cases by the project planner using his/her knowledge of domain constraints. This large-scale, case-based, and mixed-initiative planning system, integrated with intensive constraint-based adaptation, utilizes semantic-level metaconstraints and human decisions for compensating incomplete cases imbedding specific planning knowledge. The case- and constraint-based architecture inherently supports cross-checking cases with constraints during system development and maintenance. This system has drastically reduced the time and effort required for initial project planning, improved the quality and completeness of the generated plans, and is expected to give the company the competitive advantage in contract bids for new contracts.

The format of the conference will include paper presentations, invited speakers, panel discussions, workshops, and planning and scheduling competitions.

Understanding the mind is one of the great "holy grails" of twentieth-century research. Regardless of training, most people who come in contact with the field of AI are at least partially motivated by the glimmer of hope that they will get a better understanding of the mind. This quest, of course, is a rich and complex one. It is easy to get mired in minutiae along the way, be they the optimization of an algorithm, the details of a mental model, or the intricacies of a logical argument. Thagard's book attempts to call us back to the larger picture and to draw in new devotees -- and, in general, he succeeds.