The growing use of paraprofessionals as caseworkers responsible for assessment in the social services area provides fertile domain areas for new and innovative application of intelligent system technology. The main function of DISXPERT is to provide support to paraprofessional caseworkers in reaching unbiased and consistent assessment decisions regarding referral of clients to vocational rehabilitation services. The results after four years of use demonstrate that paraprofessionals using DISXPERT can make assessments in less time and with a level of accuracy superior to the vocational rehabilitation domain professionals using manual methods.

It has, for example, been interpreted in a variety of ways even within our own field, ranging from the logical view (intelligence as part of mathematical logic) to the psychological view (intelligence as an empirical phenomenon of the natural world) to a variety of others. Our physical bodies are in many ways overdetermined, unnecessarily complex, and inefficiently designed, that is, the predictable product of the blind search that is evolution. Natural intelligence is unlikely to be limited by principles of parsimony and is likely to be overdetermined, unnecessarily complex, and inefficiently designed. One example is the view that thinking is in part visual, and hence it might prove useful to develop representations and reasoning mechanisms that reason with diagrams (not just about them) and that take seriously their visual nature.

MetLife processes over 260,000 life insurance applications a year. MetLife's intelligent text analyzer (MITA) uses the information-extraction technique of natural language processing to structure the extensive textual fields on a life insurance application. MITA is currently processing 20,000 life insurance applications a month. Eighty-nine percent of the textual fields processed by MITA exceed the established confidence-level threshold and are potentially available for further analysis by domain-specific analyzers.

The emerging literature on combined systems is directed at domains where the prediction of human behavior is not required. Professionals concerned with human outcomes make decisions that are intuitive or analytic or some combination of both. Justifications and methodology are presented for combining analytic and intuitive agents in an expert system that supports professional decision making. The system presented demonstrates the challenges and opportunities inherent in developing and using AI-collaborative technology to solve social problems.

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 book probably contains more definitions than any other book I can recall reading; however, despite these definitions, one of the attractive features of the book is its reader might have been exposed when essential simplicity. One might have draft of three chapters for an first learning about probability. However, he about probability and a way that naturally of the book, but this is not found his attention increasingly distracted links probability and causality. Appendixes do present an by the possibilities provided He distinguishes the traditional events overview of the conventional by probability trees for understanding of probability theory, which he calls paradigm, and there are links throughout probability and causality--so much Moivrean events and which correspond the book between this and the so, in fact, that instead of finishing to sets of leaf nodes (or, equivalently, new approach presented here, but the the first book, he wrote a different one of paths from the root to a leaf), from book can be read without a deep grasp on this second topic. It is this second Humean events, which are steps, or of the conventional paradigm.

This article reports on the development and implementation of DISXPERT, an intelligent rule-based system tool for referral of social security disability recipients to vocational rehabilitation services. The growing use of paraprofessionals as caseworkers responsible for assessment in the social services area provides fertile domain areas for new and innovative application of intelligent system technology. The main function of DISXPERT is to provide support to paraprofessional caseworkers in reaching unbiased and consistent assessment decisions regarding referral of clients to vocational rehabilitation services. The results after four years of use demonstrate that paraprofessionals using DISXPERT can make assessments in less time and with a level of accuracy superior to the vocational rehabilitation domain professionals using manual methods. This article discusses the problem domain, the design and development of the system, uses of AI technology, payoffs, and deployment and maintenance of the system.

Tom Mitchell states that the goal of his text Machine Learning is to present the key algorithms and theory that form the core of machine learning. Not only has Mitchell suc-ceeded in his primary goal, but he has accomplished a number of other important goals.

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.

AI theory and its technology is rarely consulted in attempted resolutions of social problems. Solutions often require that decision-analytic techniques be combined with expert systems. The emerging literature on combined systems is directed at domains where the prediction of human behavior is not required. A foundational shift in AI presuppositions to intelligent agents working in collaboration provides an opportunity to explore efforts to improve the performance of social institutions that depend on accurate prediction of human behavior. Professionals concerned with human outcomes make decisions that are intuitive or analytic or some combination of both. The relative efficacy of each decision type is described. Justifications and methodology are presented for combining analytic and intuitive agents in an expert system that supports professional decision making. Psychological grounds for the allocation of functions to agents are reviewed. Jury selection, the prototype domain, is described as a process typical of others that, at their core, require the prediction of human behavior. The domain is used to demonstrate the formal components, steps in construction, and challenges of developing and testing a hybrid system based on the allocation of function. The principle that the research taught us about the allocation of function is "the rational and predictive primacy of a statistical agent to an intuitive agent in construction of a production system." We learned that the reverse of this principle is appropriate for identifying and classifying human responses to questions and generally dealing with unexpected events in a courtroom and elsewhere. This principle and approach should be paradigmatic of the class of collaborative models that capitalizes on the unique strengths of AI knowledge-based systems. The methodology used in the courtroom is described along with the history of the project and implications for the development of related AI systems. Empirical data are reported that portend the possibility of impressive predictive ability in the combined approach relative to other current approaches. Problems encountered and those remaining are discussed, including the limits of empirical research and standards of validation. The system presented demonstrates the challenges and opportunities inherent in developing and using AI-collaborative technology to solve social problems.