subjective logic a formalism for reasoning under uncertainty artificial intelligence foundations theory and algorithms

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Subjective Logic

Author : Audun Jøsang
ISBN : 9783319423371
Genre : Computers
File Size : 90. 42 MB
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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.

Ad Hoc Networks

Author : Yifeng Zhou
ISBN : 9783319744391
Genre : Computers
File Size : 37. 24 MB
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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

Author : Judea Pearl
ISBN : 9780080514895
Genre : Computers
File Size : 34. 68 MB
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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.

Theory And Algorithms For Cooperative Systems

Author : Don Grundel
ISBN : 9789814481946
Genre : Computers
File Size : 80. 3 MB
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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

Advances In The Dempster Shafer Theory Of Evidence

Author : Ronald R. Yager
ISBN : UOM:39015032917042
Genre : Law
File Size : 23. 74 MB
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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 Logic Of Adaptive Behavior

Author : M. Van
ISBN : 9781586039691
Genre : Computers
File Size : 36. 71 MB
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Learning and reasoning in large, structured, probabilistic worlds is at the heart of artificial intelligence. Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. Many efficient reinforcement learning and dynamic programming techniques exist that can solve such problems. Until recently, the representational state-of-the-art in this field was based on propositional representations.

Security And Trust Management

Author : Audun Jøsang
ISBN : 9783642380044
Genre : Computers
File Size : 67. 64 MB
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This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Workshop on Security and Trust Management, STM 2012, held in Pisa, Italy, in September 2012 - in conjunction with the 17th European Symposium Research in Computer Security (ESORICS 2012). The 20 revised full papers were carefully reviewed and selected from 57 submissions. The papers are organized into topical sections on policy enforcement and monitoring; access control; trust, reputation, and privacy; distributed systems and physical security; authentication and security policies.

Real World Reasoning Toward Scalable Uncertain Spatiotemporal Contextual And Causal Inference

Author : Ben Goertzel
ISBN : 9789491216114
Genre : Computers
File Size : 26. 43 MB
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The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.

Uncertainty In Artificial Intelligence

Author : David Heckerman
ISBN : 9781483214511
Genre : Computers
File Size : 84. 6 MB
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Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

Feature Selection For High Dimensional Data

Author : Verónica Bolón-Canedo
ISBN : 9783319218588
Genre : Computers
File Size : 53. 94 MB
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This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers. The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

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