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This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
WSC2008Chair’s Welcome Message Dear Colleague, The World Soft Computing (WSC) conference is an annual international online conference on applied and theoretical soft computing technology. This WSC 2008 is the thirteenth conference in this series and it has been a great success. We received a lot of excellent paper submissions which were peer-reviewed by an international team of experts. Only60 papers out of111 submissions were selected for online publication. This assured a high quality standard for this online conference. The corresponding online statistics are a proof of the great world-wide interest in the WSC 2008 conference. The conference website had a total of33,367di?erent human user accessesfrom43 countries with around100 visitors every day,151 people signed up to WSC to discuss their scienti?c disciplines in our chat rooms and the forum. Also audio and slide presentations allowed a detailed discussion of the papers. The submissions and discussions showed that there is a wide range of soft computing applications to date. The topics covered by the conference range from applied to theoretical aspects of fuzzy, neuro-fuzzy and rough sets over to neural networks to single and multi-objective optimisation. Contributions aboutparticleswarmoptimisation,geneexpressionprogramming,clustering, classi?cation,supportvectormachines,quantumevolutionandagentsystems have also been received. One whole session was devoted to soft computing techniques in computer graphics, imaging, vision and signal processing.
This comprehensive study of probability considers the approaches of Pascal, Laplace, Poisson, and others. It also discusses Laws of Large Numbers, the theory of errors, and other relevant topics.
This is the first fundamental book devoted to non-Kolmogorov probability models. It provides a mathematical theory of negative probabilities, with numerous applications to quantum physics, information theory, complexity, biology and psychology. The book also presents an interesting model of cognitive information reality with flows of information probabilities, describing the process of thinking, social, and psychological phenomena.
Probabilists and fuzzy enthusiasts tend to disagree about which philosophy is best and they rarely work together. As a result, textbooks usually suggest only one of these methods for problem solving, but not both. This book is an exception. The authors, investigators from both fields, have combined their talents to provide a practical guide showing that both fuzzy logic and probability have their place in the world of problem solving. They work together with mutual benefit for both disciplines, providing scientists and engineers with examples of and insight into the best tool for solving problems involving uncertainty. Fuzzy Logic and Probability Applications: Bridging the Gap makes an honest effort to show both the shortcomings and benefits of each technique, and even demonstrates useful combinations of the two. It provides clear descriptions of both fuzzy logic and probability, as well as the theoretical background, examples, and applications from both fields, making it a useful hands-on workbook for members of both camps. It contains enough theory and references to fundamental work to provide firm ground for both engineers and scientists at the undergraduate level and above. Readers should have a familiarity with mathematics through calculus.
This volume has two primary aims: to trace the traditions and changes in methods, concepts, and ideas that brought forth the logical empiricists’ philosophy of physics and to present and analyze the logical empiricists’ various and occasionally contrary ideas about the physical sciences and their philosophical relevance. These original chapters discuss these developments in their original contexts and social and institutional environments, thus showing the various fruitful conceptions and philosophies behind the history of 20th-century philosophy of science. Logical Empiricism and the Natural Sciences is divided into three thematic sections. Part I surveys the influences on logical empiricism’s philosophy of science and physics. It features chapters on Maxwell’s role in the worldview of logical empiricism, on Reichenbach’s account of objectivity, on the impact of Poincaré on Neurath’s early views on scientific method, Frank’s exchanges with Einstein about philosophy of physics, and on the forgotten role of Kurt Grelling. Part II focuses on specific physical theories, including Carnap’s and Reichenbach’s positions on Einstein’s theory of general relativity, Reichenbach’s critique of unified field theory, and the logical empiricists’ reactions to quantum mechanics. The third and final group of chapters widens the scope to philosophy of science and physics in general. It includes contributions on von Mises’ frequentism; Frank’s account of concept formation and confirmation; and the interrelations between Nagel’s, Feigl’s, and Hempel’s versions of logical empiricism. This book offers a comprehensive account of the logical empiricists’ philosophy of physics. It is a valuable resource for researchers interested in the history and philosophy of science, philosophy of physics, and the history of analytic philosophy.
This volume composes the proceedings of the Second International Conference on Computational Collective Intelligence––Technologies and Applications (ICCCI 2010), which was hosted by National Kaohsiung University of Applied Sciences and Wroclaw University of Technology, and was held in Kaohsiung City on November 10-12, 2010. ICCCI 2010 was technically co-sponsored by Shenzhen Graduate School of Harbin Institute of Technology, the Tainan Chapter of the IEEE Signal Processing Society, the Taiwan Association for Web Intelligence Consortium and the Taiwanese Association for Consumer Electronics. It aimed to bring together researchers, engineers and po- cymakers to discuss the related techniques, to exchange research ideas, and to make friends. ICCCI 2010 focused on the following themes: • Agent Theory and Application • Cognitive Modeling of Agent Systems • Computational Collective Intelligence • Computer Vision • Computational Intelligence • Hybrid Systems • Intelligent Image Processing • Information Hiding • Machine Learning • Social Networks • Web Intelligence and Interaction Around 500 papers were submitted to ICCCI 2010 and each paper was reviewed by at least two referees. The referees were from universities and industrial organizations. 155 papers were accepted for the final technical program. Four plenary talks were kindly offered by: Gary G. Yen (Oklahoma State University, USA), on “Population Control in Evolutionary Multi-objective Optimization Algorithm,” Chin-Chen Chang (Feng Chia University, Taiwan), on “Applying De-clustering Concept to Information Hiding,” Qinyu Zhang (Harbin Institute of Technology, China), on “Cognitive Radio Networks and Its Applications,” and Lakhmi C.
These Transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the Semantic Web, social networks and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This fourth issue contains a collection of 6 articles selected from high-quality submissions. The first paper of Ireneusz Czarnowski entitled "Distributed Learning with Data Reduction" consists of 120 pages and has a monograph chracter. The second part consists of five regular papers adressing advances in the foundations and applications of computational collective intelligence.
Why is understanding causation so important in philosophy and the sciences? Should causation be defined in terms of probability? Whilst causation plays a major role in theories and concepts of medicine, little attempt has been made to connect causation and probability with medicine itself. Causality, Probability, and Medicine is one of the first books to apply philosophical reasoning about causality to important topics and debates in medicine. Donald Gillies provides a thorough introduction to and assessment of competing theories of causality in philosophy, including action-related theories, causality and mechanisms, and causality and probability. Throughout the book he applies them to important discoveries and theories within medicine, such as germ theory; tuberculosis and cholera; smoking and heart disease; the first ever randomized controlled trial designed to test the treatment of tuberculosis; the growing area of philosophy of evidence-based medicine; and philosophy of epidemiology. This book will be of great interest to students and researchers in philosophy of science and philosophy of medicine, as well as those working in medicine, nursing and related health disciplines where a working knowledge of causality and probability is required.
This volume provides a philosophical appraisal of probabilities in all of physics. It makes sense of probabilistic statements as they occur in the various physical theories and models and presents a plausible epistemology and metaphysics of probabilities.