Download or read book Representing Uncertain Knowledge written by Paul Krause and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: The representation of uncertainty is a central issue in Artificial Intelligence (AI) and is being addressed in many different ways. Each approach has its proponents, and each has had its detractors. However, there is now an in creasing move towards the belief that an eclectic approach is required to represent and reason under the many facets of uncertainty. We believe that the time is ripe for a wide ranging, yet accessible, survey of the main for malisms. In this book, we offer a broad perspective on uncertainty and approach es to managing uncertainty. Rather than provide a daunting mass of techni cal detail, we have focused on the foundations and intuitions behind the various schools. The aim has been to present in one volume an overview of the major issues and decisions to be made in representing uncertain knowl edge. We identify the central role of managing uncertainty to AI and Expert Systems, and provide a comprehensive introduction to the different aspects of uncertainty. We then describe the rationales, advantages and limitations of the major approaches that have been taken, using illustrative examples. The book ends with a review of the lessons learned and current research di rections in the field. The intended readership will include researchers and practitioners in volved in the design and implementation of Decision Support Systems, Ex pert Systems, other Knowledge-Based Systems and in Cognitive Science.
Download or read book Uncertainty and Vagueness in Knowledge Based Systems written by Rudolf Kruse and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary aim of this monograph is to provide a formal framework for the representation and management of uncertainty and vagueness in the field of artificial intelligence. It puts particular emphasis on a thorough analysis of these phenomena and on the development of sound mathematical modeling approaches. Beyond this theoretical basis the scope of the book includes also implementational aspects and a valuation of existing models and systems. The fundamental ambition of this book is to show that vagueness and un certainty can be handled adequately by using measure-theoretic methods. The presentation of applicable knowledge representation formalisms and reasoning algorithms substantiates the claim that efficiency requirements do not necessar ily require renunciation of an uncompromising mathematical modeling. These results are used to evaluate systems based on probabilistic methods as well as on non-standard concepts such as certainty factors, fuzzy sets or belief functions. The book is intended to be self-contained and addresses researchers and practioneers in the field of knowledge based systems. It is in particular suit able as a textbook for graduate-level students in AI, operations research and applied probability. A solid mathematical background is necessary for reading this book. Essential parts of the material have been the subject of courses given by the first author for students of computer science and mathematics held since 1984 at the University in Braunschweig.
Download or read book Reasoning about Uncertainty second edition written by Joseph Y. Halpern and published by MIT Press. This book was released on 2017-04-07 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.
Download or read book Representing and Reasoning with Probabilistic Knowledge written by Fahiem Bacchus and published by Cambridge, Mass. : MIT Press. This book was released on 1990 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general. Fahiem Bacchus focuses on two distinct notions of probabilities: one propositional, involving degrees of belief, the other proportional, involving statistics. He constructs distinct logics with different semantics for each type of probability that are a significant advance in the formal tools available for representing and reasoning with probabilities. These logics can represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilities, and they can represent firstshy;order logical information. The logics also have proof theories which give a formal specification for a class of reasoning that subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI. Using the new logical tools to connect statistical with propositional probability, Bacchus also proposes a system of direct inference in which degrees of belief can be inferred from statistical knowledge and demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning. Fahiem Bacchus is Assistant Professor of Computer Science at the University of Waterloo, Ontario. Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.
Download or read book Uncertainty in Artificial Intelligence written by Laveen N. Kanal and published by North Holland. This book was released on 1986 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.
Download or read book Artificial Intelligence with Uncertainty written by Deyi Li and published by CRC Press. This book was released on 2017-05-18 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language – the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.
Download or read book Artificial Intelligence written by Ela Kumar and published by I. K. International Pvt Ltd. This book was released on 2013-12-30 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI is an emerging discipline of computer science. It deals with the concepts and methodologies required for computer to perform an intelligent activity. The spectrum of computer science is very wide and it enables the computer to handle almost every activity, which human beings could. It deals with defining the basic problem from viewpoint of solving it through computer, finding out the total possibilities of solution, representing the problem from computational orientation, selecting data structures, finding the solution through searching the goal in search space dealing the real world uncertain situations etc. It also develops the techniques for learning and understanding, which make the computer able to exhibit an intelligent behavior. The list is exhaustive and is applied now a days in almost every field of technology. This book presents almost all the components of AI like problem solving, search techniques, knowledge concepts, expert system and many more in a very simple language. One of the unique features of this book is inclusion of number of solved examples; in between the chapters and also at the end of many chapters. Real life examples have been discussed to make the reader conversant with the intricate phenomenon of computer science in general, and artificial intelligence in particular. The book is primarily developed for undergraduate and postgraduate engineering students.
Download or read book Probabilistic Reasoning in Expert Systems written by Richard E. Neapolitan and published by CreateSpace. This book was released on 2012-06-01 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.
Download or read book Uncertainty in Artificial Intelligence written by Didier J. Dubois and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.
Download or read book Quantified Representation of Uncertainty and Imprecision written by Dov M. Gabbay and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are happy to present the first volume of the Handbook of Defeasible Reasoning and Uncertainty Management Systems. Uncertainty pervades the real world and must therefore be addressed by every system that attempts to represent reality. The representation of uncertainty is a ma jor concern of philosophers, logicians, artificial intelligence researchers and com puter sciencists, psychologists, statisticians, economists and engineers. The present Handbook volumes provide frontline coverage of this area. This Handbook was produced in the style of previous handbook series like the Handbook of Philosoph ical Logic, the Handbook of Logic in Computer Science, the Handbook of Logic in Artificial Intelligence and Logic Programming, and can be seen as a companion to them in covering the wide applications of logic and reasoning. We hope it will answer the needs for adequate representations of uncertainty. This Handbook series grew out of the ESPRIT Basic Research Project DRUMS II, where the acronym is made out of the Handbook series title. This project was financially supported by the European Union and regroups 20 major European research teams working in the general domain of uncertainty. As a fringe benefit of the DRUMS project, the research community was able to create this Hand book series, relying on the DRUMS participants as the core of the authors for the Handbook together with external international experts.
Download or read book Scalable Uncertainty Management written by Salem Benferhat and published by Springer. This book was released on 2011-10-07 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Scalable Uncertainty Management, SUM 2011, held in Dayton, OH, USA, in October 2011. The 32 revised full papers and 3 revised short papers presented together with the abstracts of 2 invited talks and 6 “discussant” contributions were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on argumentation systems, probabilistic inference, dynamic of beliefs, information retrieval and databases, ontologies, possibility theory and classification, logic programming, and applications.
Download or read book Probability and Bayesian Modeling written by Jim Albert and published by CRC Press. This book was released on 2019-12-06 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
Download or read book Uncertainty in Artificial Intelligence written by L.N. Kanal and published by Elsevier. This book was released on 2014-06-28 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.
Download or read book Symbolic and Quantitative Approaches to Reasoning with Uncertainty written by Lluis Godo and published by Springer Science & Business Media. This book was released on 2005-06-24 with total page 1043 pages. Available in PDF, EPUB and Kindle. Book excerpt: These are the proceedings of the 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2005, held in Barcelona (Spain), July 6–8, 2005. The ECSQARU conferences are biennial and have become a major forum for advances in the theory and practice of r- soning under uncertainty. The ?rst ECSQARU conference was held in Marseille (1991), and after in Granada (1993), Fribourg (1995), Bonn (1997), London (1999), Toulouse (2001) and Aalborg (2003). The papers gathered in this volume were selected out of 130 submissions, after a strict review process by the members of the Program Committee, to be presented at ECSQARU 2005. In addition, the conference included invited lectures by three outstanding researchers in the area, Seraf ́ ?n Moral (Imprecise Probabilities), Rudolf Kruse (Graphical Models in Planning) and J ́ erˆ ome Lang (Social Choice). Moreover, the application of uncertainty models to real-world problems was addressed at ECSQARU 2005 by a special session devoted to s- cessful industrial applications, organized by Rudolf Kruse. Both invited lectures and papers of the special session contribute to this volume. On the whole, the programme of the conference provided a broad, rich and up-to-date perspective of the current high-level research in the area which is re?ected in the contents of this volume. IwouldliketowarmlythankthemembersoftheProgramCommitteeandthe additional referees for their valuable work, the invited speakers and the invited session organizer.
Download or read book Uncertainty in Artificial Intelligence 4 written by T.S. Levitt and published by Elsevier. This book was released on 2014-06-28 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.
Download or read book Uncertainty Reasoning for the Semantic Web II written by Fernando Bobillo and published by Springer. This book was released on 2013-01-09 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Federated Logic Conference (FLoC) in 2010. The 17 papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.
Download or read book Nonmonotonic Reasoning written by Grigoris Antoniou and published by MIT Press. This book was released on 1997 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonmonotonic reasoning provides formal methods that enable intelligent systems to operate adequately when faced with incomplete or changing information. In particular, it provides rigorous mechanisms for taking back conclusions that, in the presence of new information, turn out to be wrong and for deriving new, alternative conclusions instead. Nonmonotonic reasoning methods provide rigor similar to that of classical reasoning; they form a base for validation and verification and therefore increase confidence in intelligent systems that work with incomplete and changing information. Following a brief introduction to the concepts of predicate logic that are needed in the subsequent chapters, this book presents an in depth treatment of default logic. Other subjects covered include the major approaches of autoepistemic logic and circumscription, belief revision and its relationship to nonmonotonic inference, and briefly, the stable and well-founded semantics of logic programs.
