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Methods and Applications of Artificial Intelligence
This book constitutes the refereed proceedings of the Second Hellenic Conference on Artificial Intelligence, SETN 2002, held in Thessaloniki, Greece, in April 2002. The 42 revised full papers presented together with two invited contributions were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on knowledge representation and reasoning, logic programming and constraint satisfaction, planning and scheduling, natural language processing, human-computer interaction, machine learning, intelligent Internet and multiagent systems, and intelligent applications.
Engineering Systems with Intelligence
This book contains a selection of papers presented at the "European Robotics and Intelligent Systems Conference" (EURISCON '91) held in Corfu. Greece (June 23-28. 1991). It is devoted to the analysis. design and applications of technological systems with built-in intelligence achieved through appropriate blending of mathematical, symbolic. sensing. computer processing. and feedback control concepts. methods and software / hardware tools. System intelligence includes human-like capabilities such as learning. observation. perception. interpretation. reasoning. planning. decision making. and action. Integrated intelligent decision and control systems obey Saridis' prinCiple of Increasing Precis...
Fuzzy Sets In Management, Economics And Marketing
The rapid changes that have taken place globally on the economic, social and business fronts characterized the 20th century. The magnitude of these changes has formed an extremely complex and unpredictable decision-making framework, which is difficult to model through traditional approaches. The main purpose of this book is to present the most recent advances in the development of innovative techniques for managing the uncertainty that prevails in the global economic and management environments. These techniques originate mainly from fuzzy sets theory. However, the book also explores the integration of fuzzy sets with other decision support and modeling disciplines, such as multicriteria decision aid, neural networks, genetic algorithms, machine learning, chaos theory, etc. The presentation of the advances in these fields and their real world applications adds a new perspective to the broad fields of management science and economics.
Managing in Uncertainty: Theory and Practice
This book provides a new point of view on the subject of the management of uncertainty. It covers a wide variety of both theoretical and practical issues involving the analysis and management of uncertainty in the fields of finance, management and marketing. Audience: Researchers and professionals from operations research, management science and economics.
Encyclopedia of Information Science and Technology, Third Edition
"This 10-volume compilation of authoritative, research-based articles contributed by thousands of researchers and experts from all over the world emphasized modern issues and the presentation of potential opportunities, prospective solutions, and future directions in the field of information science and technology"--Provided by publisher.
GI for Disaster Management
Each year, disasters such as storms, floods, fires, volcanoes, earthquakes, and epidemics cause thousands of casualties and tremendous damage to property around the world, displacing tens of thousands of people from their homes and destroying their livelihoods. The majority of these casualties and property loss could be prevented if better information were available regarding the onset and course of such disasters. Several remote sensing technologies, such as meteorological and Earth observation satellites, communication satellites, and satellite-based positioning, supported by geoinformation technologies, offer the potential to contribute to improved prediction and monitoring of potential h...
Data Mining and Computational Intelligence
Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a ...
Research and Development in Intelligent Systems XXIII
The papers in this volume are the refereed technical papers presented at AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2006. They present new and innovative developments in the field. For the first time the volume also includes the text of short papers presented as posters at the conference.
Computational Intelligence Systems and Applications
Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of...
