Knowledge Integration Methods For Probabilistic Knowledge Based Systems
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Knowledge Integration Methods for Probabilistic Knowledge-based Systems by Van Tham Nguyen,Ngoc Thanh Nguyen,Trong Hieu Tran Pdf
Knowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. Knowledge Integration Methods provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. The book also provides the mathematical background to solving problems of restoring consistency and integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. This text will be useful for computer science graduates and PhD students, in addition to researchers and readers working on knowledge management and ontology interpretation.
Learning Bayesian Networks by Richard E. Neapolitan Pdf
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Author : Linda C. van der Gaag,Ad J. Feelders Publisher : Springer Page : 609 pages File Size : 51,6 Mb Release : 2014-09-11 Category : Computers ISBN : 9783319114330
Probabilistic Graphical Models by Linda C. van der Gaag,Ad J. Feelders Pdf
This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning.
Computational Collective Intelligence by Ngoc Thanh Nguyen,Elias Pimenidis,Zaheer Khan,Bogdan Trawiński Pdf
This two-volume set (LNAI 11055 and LNAI 11056) constitutes the refereed proceedings of the 10th International Conference on Collective Intelligence, ICCCI 2018, held in Bristol, UK, in September 2018 The 98 full papers presented were carefully reviewed and selected from 240 submissions. The conference focuses on knowledge engineering and semantic web, social network analysis, recommendation methods and recommender systems, agents and multi-agent systems, text processing and information retrieval, data mining methods and applications, decision support and control systems, sensor networks and internet of things, as well as computer vision techniques.
Uncertain Information Processing In Expert Systems by Petr Hajek,Tomas Havranek,Radim Jirousek Pdf
Uncertain Information Processing in Expert Systems systematically and critically examines probabilistic and rule-based (compositional, MYCIN-like) systems, the two most important families of expert systems dealing with uncertainty. The book features a detailed introduction to probabilistic systems (including methods using graphical models and methods of knowledge integration), an analysis of compositional systems based on algebraic considerations, an application of graphical models, and the Dempster-Shafer theory of evidence and its use in expert systems. The book will be useful to anyone working in artificial intelligence, statistical computing, symbolic logic, and expert systems.
Utilizing Data and Knowledge Mining for Probabilistic Knowledge Bases by Daniel Joseph Stein Pdf
Problems can arise whenever inferencing is attempted on a knowledge base that is incomplete. Our work shows that data mining techniques can be applied to fill in incomplete areas in Bayesian Knowledge Bases (BKBs), as well as in other knowledge-based systems utilizing probabilistic representations. The problem of inconsistency in BKBs has been addressed in previous work, where reinforcement learning techniques from neural networks were applied. However, the issue of automatically solving incompleteness in BKBs has yet to be addressed. Presently, incompleteness in BKBs is repaired through the application of traditional knowledge acquisition techniques. We show how association rules can be extracted from databases in order to replace excluded information and express missing relationships. A methodology for incorporating those results while maintaining a consistent knowledge base is also included.
Advanced Topics in Artificial Intelligence by Vladimír Mařík,Olga Štěpánková,Robert Trappl Pdf
"This volume contains the texts of 26 lectures and contributions to the program of the International Summer School on Advanced Topics in Artificial Intelligence held in Prague, Czechoslovakia, July 6-17, 1992. The summerschool was intended for (postgraduate) students, researchers and all those who want to learn about recent progress in both theoretical and applied AI. The papers in the volume are organized into nine parts: - Introduction - Logic and logic programming - Machine learning - Planning and scheduling - Uncertainty - Second generation expert systemsand knowledge engineering - Qualitative reasoning - Neurocomputing -Natural language and interfaces"--PUBLISHER'S WEBSITE.
A Methodology for Uncertainty in Knowledge-Based Systems by Kurt Weichselberger,Sigrid Pöhlmann Pdf
In this book the consequent use of probability theory is proposed for handling uncertainty in expert systems. It is shown that methods violating this suggestion may have dangerous consequences (e.g., the Dempster-Shafer rule and the method used in MYCIN). The necessity of some requirements for a correct combining of uncertain information in expert systems is demonstrated and suitable rules are provided. The possibility is taken into account that interval estimates are given instead of exact information about probabilities. For combining information containing interval estimates rules are provided which are useful in many cases.
The Knowledge-based Economy by Loet Leydesdorff Pdf
"Challenging, theoretically rich yet anchored in detailed empirical analysis, Loet Leydesdorff's exploration of the dynamics of the knowledge-economy is a major contribution to the field. Drawing on his expertise in science and technology studies, systems theory, and his internationally respected work on the 'triple helix', the book provides a radically new modelling and simulation of knowledge systems, capturing the articulation of structure, communication, and agency therein. This work will be of immense interest to both theorists of the knowledge-economy and practitioners in science policy." Andrew Webster Science & Technology Studies, University of York, UK ________________________________________ "This book is a ground-breaking collection of theory and techniques to help understand the internal dynamics of the modern knowledge-based economy, including issues such as stability, anticipation, and interactions amongst components. The combination of theory, measurement, and modelling gives the necessary power with which to address the complexity of modern networked social systems. Each on its own would partly illuminate an innovation system, but the combination sheds a far brighter light." Mike Thelwall Information Science, University of Wolverhampton, UK ________________________________________ "The sociologist Niklas Luhmann is considered one of the few social scientists possibly able to explain a decisive event once it has happened. In this book, Loet Leydesdorff answers the challenge to take Luhmann's analysis one step further by introducing anticipation into the theory. This book provides a fascinating exploration of the use of recursion and incursion to model social processes." Dirk Baecker Sociology, Universität Witten/Herdecke, Germany ________________________________________ How can an economy based on something as volatile as knowledge be sustained? The urgency of improving our understanding of a knowledge-based economy provides the context and necessity of this study. In a previous study entitled A Sociological Theory of Communications: The Self-Organization of the Knowledge-based Society (2001) the author specified knowledge-based systems from a sociological perspective. In this book, he takes this theory one step further and demonstrates how the knowledge base of an economic system can be operationalized, both in terms of measurement and by providing simulation models.
