These software between telephone, television, agents are on their way, and they're going to The be getting a lot of things accomplished by basic idea is that the networks that constitute interacting with each other. The question is, our telephone infrastructure, our television How will these agents be cooperating with (particularly cable) infrastructure, and our each other, competing with each other, and computer infrastructure will be coalescing into negotiating with each other? Now, the agents that we are interested in Another example is routing among looking at are heterogeneous, self-motivated telecommunication networks. The systems are not assumed to be packets, can pass over a network controlled by centrally designed. For example, if you have a one company onto another network controlled personal digital assistant, you might have one by another company, or it can pass that was built by IBM, but the next person through one country on through another. Computers that control a telecommunications They don't necessarily have a notion of global network might find it beneficial to enter into utility. Each personal digital assistant or agreements with other computers that control each agent operating from your machine is other networks about routing packets more interested in what your idea of utility is and efficiently from source to destination. The in how to further your notion of goodness. We're other agents ask them to do unless they have Another example is the proliferation of shared databases, where there's information They have sprung up with a vengeance in the last decade.

This report stems from an April 1994 meeting, organized by AAAI at the suggestion of Steve Cross and Gio Wiederhold.1 The purpose of the meeting was to assist ARPA in defining an agenda for foundational AI research. Prior to the meeting, the fellows and officers of AAAI, as well as the report committee members, were asked to recommend areas in which major research thrusts could yield significant scientific gain -- with high potential impact on DOD applications -- over the next ten years. At the meeting, these suggestions and their relevance to current national needs and challenges in computing were discussed and debated. An initial draft of this report was circulated to the fellows and officers. The final report has benefited greatly from their comments and from textual revisions contributed by Joseph Halpern, Fernando Pereira, and Dana Nau.

This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.

Information extraction is the task of automaticallypicking up information of interest from an unconstrained text. Informationof interest is usually extracted in two steps. First, sentence level processing locates relevant pieces of information scatteredthroughout the text; second, discourse processing merges coreferential information to generate the output. In the first step, pieces of information are locally identified without recognizing any relationships among them. A key word search or simple patternsearch can achieve this purpose. The second step requires deeperknowledge in order to understand relationships among separately identified pieces of information. Previous information extraction systems focused on the first step, partly because they were not required to link up each piece of information with other pieces. To link the extracted pieces of information and map them onto a structuredoutput format, complex discourse processing is essential. This paperreports on a Japanese information extraction system that merges information using a pattern matcher and discourse processor. Evaluationresults show a high level of system performance which approaches human performance.

Southwest Research Institute and the U.S. Air Force Materiel Command designed and developed an automated system for the preparation of deficiency report analysis information reports (DRAIRs). A DRAIR provides Air Force engineers with an analysis of an aircraft item's performance history, including maintenance, supply, and cost. A DRAIR also recommends improvements for a deficient materiel or aircraft part. The successful design, development, and deployment of the DRAIR ADVISER system by applying a combination of knowledge-based system and database management techniques are the subject of this article.

The human orientation system is a complex system in which the brain merges information from a variety of sensors to help maintain a coherent interpretation of body position and movement. These sensors include the semicircular canals and the otolith organs located in the inner ear as well as vision and somatosensory perception. I designed a model of this system based on the observer theory model (OTM), which was developed by Merfeld (1990) for the orientation system of the squirrel monkey. Under this scheme, the central nervous system has an internal representation of the sensor organs and tries to minimize the error between its estimate of the sensory afferent signals and the actual afferent signals. As designed, MARIKA's goal is to classify the vestibular system of the subject as normal or abnormal and propose a corresponding model. It works iteratively until the results of the proposed experiment can be modeled. Additional experiments can be presented in succession to the same model.

The Hong Kong-based Mass Transit Railway Corp. (MTRC) has developed the Station Management Expert e Norwegian Police Data Center help predict aircraft fires and other System (SMES). SMES is an intelligent utilized an expert system to catastrophes. The police put and risk factors from the records functions and advising the controller the intelligent application online to of the National Transportation Safety of actions to take in case of emergency. The system is installed in Ya Ma at the games while complying with Carnegie Group and Westinghouse Tei Station as a test site, and the complex national employment regulations. Electric (both in Pittsburgh, Penn.) are MTRC plans to expand its use Plans are to deploy and network working with Pittsburgh area medical throughout the subway system as it the expert system into every law centers to develop an intelligent proves to be successful. The network Martin Marietta (Bethesda, Md.) is developed a neural network application will gather and organize data on using a real-time expert system to that has improved the efficiency clinical diagnoses, treatment, clinical build the Traffic Operations Center of its direct mail marketing efforts by and research findings, and patient (TOC) component of its Intelligent 35%.

The Fifth International Conference on Genetic Algorithms was held at the University of Illinois at Urbana-Champaign from 17-21 July 1993. Approximately 350 participants attended the multitrack conference, which covered a wide range of topics, including genetic operators, mathematical analysis of genetic algorithms, parallel genetic algorithms, classifier systems, and genetic programming. This article highlights the major themes of the conference by discussing a few papers in detail.

Engineers Doing so would reduce demands on the OR and equipment specialists responsible for the analysts and provide additional time for them troublesome part, or end item, review the to address more complex analysis problems. MDR to identify the possible cause(s) of failure. Further, with the turnover of personnel in the In the past, engineers and equipment military and the aging of the aircraft fleet, specialists have turned to operations research another objective was to capture expertise (OR) analysts to assist in item performance from personnel who are most knowledgeable analysis. This analysis is usually time consuming about specific aircraft systems and federal and personnel intensive and requires stock classes (FSCs) and make this expertise information from many Air Force data systems. Center (ALC), located at Tinker Air Force Base, data collection and analysis require two person-days. This document describes an item's to the automation of SOURCE DATA: The data used to prepare this report came from the following sources: 1) Product Performance Subsystem (G099), 2) Supportability analysis Forecasting Evaluation (SAFE), 3) Flying Hours (G099), 4) MICAP Hours (D165B), and 5) VAMOSC (D160B). MAINTENANCE DATA (D056): A total of 175 inherent failures occurred between JUL 1991 and JUN 1992, which translates into a Mean Time Between Maintenance Type-1 (MTBM-1) of 162 hours.

Real-time problem solving is not only reasoning about time, it is also reasoning in time. This ability is becoming increasingly critical in systems that monitor and control complex processes in semiautonomous, ill-structured, real-world environments. Many techniques, mostly ad hoc, have been developed in both the real-time community and the AI community for solving problems within time constraints. However, a coherent, holistic picture does not exist. This article is an attempt to step back from the details and examine the entire issue of real-time problem solving from first principles. We examine the degrees of freedom available in structuring the problem space and the search process to reduce problem-solving variations and produce satisficing solutions within the time available. This structured approach aids in understanding and sorting out the relevance and utility of different real-time problem-solving techniques.