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This is the first comprehensive treatment of subjective logic and all its operations. The author developed the approach, and in this book he first explains subjective opinions, opinion representation, and decision-making under vagueness and uncertainty, and he then offers a full definition of subjective logic, harmonising the key notations and formalisms, concluding with chapters on trust networks and subjective Bayesian networks, which when combined form general subjective networks. The author shows how real-world situations can be realistically modelled with regard to how situations are perceived, with conclusions that more correctly reflect the ignorance and uncertainties that result from partially uncertain input arguments. The book will help researchers and practitioners to advance, improve and apply subjective logic to build powerful artificial reasoning models and tools for solving real-world problems. A good grounding in discrete mathematics is a prerequisite.
This book constitutes the refereed proceedings of the 9th International Conference on Ad Hoc Networks, AdHocNets 2017, held in Niagara Falls, Ontario, USA, in September 2017. The 19 full papers were selected from 30 submissions and cover a variety of network paradigms including mobile ad hoc networks (MANETs), sensor networks, vehicular networks, underwater networks, airborne networks, underground networks, personal area networks, device-to-device (D2D) communications in 5G cellular networks, and home networks. The papers present a wide range of applications in civilian, commercial, and military areas.
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Over the past several years, cooperative control and optimization have increasingly played a larger and more important role in many aspects of military sciences, biology, communications, robotics, and decision making. At the same time, cooperative systems are notoriously difficult to model, analyze, and solve — while intuitively understood, they are not axiomatically defined in any commonly accepted manner. The works in this volume provide outstanding insights into this very complex area of research. They are the result of invited papers and selected presentations at the Fourth Annual Conference on Cooperative Control and Optimization held in Destin, Florida, November 2003. This book has been selected for coverage in: • Index to Scientific & Technical Proceedings® (ISTP® / ISI Proceedings) • Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings) • CC Proceedings — Engineering & Physical Sciences Contents:Mesh Stability in Formation of Distributed Systems (C Ashokkumar et al.)On the Performance of Heuristics for Broadcast Scheduling (C Commander et al.)Coupled Detection Rates: An Introduction (D Jeffcoat)Decentralized Receding Horizon Control for Multiple UAVs (Y Kuwata & J How)Multitarget Sensor Management of Dispersed Mobile Sensors (R Mahler)K-Means Clustering Using Entropy Minimization (A Okafor & P Pardalos)Possibility Reasoning and the Cooperative Prisoner's Dilemma (H Pfister & J Walls)Coordinating Very Large Groups of Wide Area Search Munitions (P Scerri et al.)A Vehicle Following Methodology for UAV Formations (S Spry et al.)Decentralized Optimization via Nash Bargaining (S Waslander et al.)and other papers Readership: Graduate students and researchers in optimization and control, computer science and engineering. Keywords:Cooperative Systems, Cooperative Control;Optimization;Cooperative NetworksKey Features:25 chapters of creative approaches to modeling, analysis, and synthesis of cooperative systemsResearch results from top researchers in the field of cooperative systemsExciting insights to cooperative systems which have increasingly played a larger and more important role in many aspects of military sciences, biology, communications, robotics, and decision making
Builds on classical probability theory and offers an extremely workable solution to the many problems of artificial intelligence, concentrating on the rapidly growing areas of fuzzy reasoning and neural computing. Contains a collection of previously unpublished articles by leading researchers in the field.
The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.
For almost 2,500 years, the Western concept of what is to be human has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. In this text a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward.

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