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The increasing complexity of our world demands new perspectives on the role of technology in decision making. Human decision making has its li- tations in terms of information-processing capacity. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and tra?c management, where humans need to engage in close collaborations with arti?cial systems to observe and understand the situation and respond in a sensible way. We believe that close collaborations between humans and arti?cial systems will become essential and that the importance of research into Interactive Collaborative Information Systems (ICIS) is self-evident. Developments in information and communication technology have ra- cally changed our working environments. The vast amount of information available nowadays and the wirelessly networked nature of our modern so- ety open up new opportunities to handle di?cult decision-making situations such as computer-supported situation assessment and distributed decision making. To make good use of these new possibilities, we need to update our traditional views on the role and capabilities of information systems. The aim of the Interactive Collaborative Information Systems project is to develop techniques that support humans in complex information en- ronments and that facilitate distributed decision-making capabilities. ICIS emphasizes the importance of building actor-agent communities: close c- laborations between human and arti?cial actors that highlight their comp- mentary capabilities, and in which task distribution is ?exible and adaptive.
This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.
presents a unified and in-depth development of neural network learning algorithms and neural network expert systems
Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.
In the two decades since its inception by L. Zadeh, the theory of fuzzy sets has matured into a wide-ranging collection of concepts, models, and tech niques for dealing with complex phenomena which do not lend themselves to analysis by classical methods based on probability theory and bivalent logic. Nevertheless, a question which is frequently raised by the skeptics is: Are there, in fact, any significant problem areas in which the use of the theory of fuzzy sets leads to results which could not be obtained by classical methods? The approximately 5000 publications in this area, which are scattered over many areas such as artificial intelligence, computer science, control engineering, decision making, logic, operations research, pattern recognition, robotics and others, provide an affirmative answer to this question. In spite of the large number of publications, good and comprehensive textbooks which could facilitate the access of newcomers to this area and support teaching were missing until recently. To help to close this gap and to provide a textbook for courses in fuzzy set theory which can also be used as an introduction to this field, the first volume ofthis book was published in 1985 [Zimmermann 1985 b]. This volume tried to cover fuzzy set theory and its applications as extensively as possible. Applications could, therefore, only be described to a limited extent and not very detailed.
Expert system technology is receiving increasing popularity and acceptance in the engineering community. This is due to the fact that there actually exists a close match between the capabilities of the current generation expert systems and the requirements of engineering practice. Prepared by a distinguished team of experts, this book provides a balanced state-of-the-art presentation of the design principles of engineering expert systems, and a representative picture of their capabilities to assist efficiently the design, diagnosis and operation of complex industrial plants. Among the application areas covered are the following: hardware synthesis, industrial plant layout design, fault diagnosis, process control, image analysis, computer communication, electric power systems, intelligent control, robotics, and manufacturing systems. The book is appropriate for the researcher and the professional. The researcher can save considerable time in searching the scattered technical information on engineering expert systems. The professional can have readily available a rich set of guidelines and techniques that are applicable to a wide class of engineering domains.
Introduces the concept of expert systems development as a model for the acquisition, representation and validation of knowledge about relatively limited domains. Case studies derive from the authors' own development experiences in the social sciences.
This two volume set LNCS 9261 and LNCS 9262 constitutes the refereed proceedings of the 26th International Conference on Database and Expert Systems Applications, DEXA 2015, held in Valencia, Spain, September 1-4, 2015. The 40 revised full papers presented together with 32 short papers, and 2 keynote talks, were carefully reviewed and selected from 125 submissions. The papers discuss a range of topics including: temporal, spatial and high dimensional databases; semantic Web and ontologies; modeling, linked open data; NoSQLm NewSQL, data integration; uncertain data and inconsistency tolerance; database system architecture; data mining, query processing and optimization; indexing and decision support systems; modeling, extraction, social networks; knowledge management and consistency; mobility, privacy and security; data streams, Web services; distributed, parallel and cloud databases; information retrieval; XML and semi-structured data; data partitioning, indexing; data mining, applications; WWW and databases; data management algorithms. These volumes also include accepted papers of the 8th International Conference on Data Management in Cloud, Grid and P2P Systems, Globe 2015, held in Valencia, Spain, September 2, 2015. The 8 full papers presented were carefully reviewed and selected from 13 submissions. The papers discuss a range of topics including: MapReduce framework: load balancing, optimization and classification; security, data privacy and consistency; query rewriting and streaming.