Search results for: representing-uncertain-knowledge

Representing Uncertain Knowledge

Author : Paul Krause
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This book identifies the central role of managing uncertainty in AI and expert systems and provides a comprehensive introduction to different aspects of uncertainty and the rationales, descriptions (through worked examples), advantages and limitations of the major approaches that have been taken. The book introduces and describes the main ways in which uncertainty can occur and the importance of managing uncertainty for the production of intelligent behaviour in AI and its associated technologies of knowledge-based systems. It also describes the rationale, advantages and limitations of the major representational approaches (both quantitative and symbolic) that have been employed in AI systems and provides a worked illustration of each method. Finally, the book summarises the significant themes that have emerged from applications and the research literature and identifies current and future directions. The book, the first to concentrate wholly on this specific area of Artificial Intelligence, is aimed primarily at researchers and practitioners involved in the design and implementation of expert systems, other knowledge-based systems and cognitive science. It will also be of value to students of computer science, cognitive science, psychology and engineering with an interest in AI or decision support systems. While a technical book, technical details are presented in appendices, allowing the text to be read continuously by nontechnical readers. (abstract) This book assigns the central role of managing uncertainty to AI and expert systems while providing a comprehensive introduction to different aspects of uncertainty. The rationales, advantages and limitations of the major approaches to managing and reasoning under uncertainty are described using worked examples.

Representing Uncertain Knowledge

Author : Paul Krause
File Size : 83.56 MB
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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.

Discovery And Fusion Of Uncertain Knowledge In Data

Author : Yue Kun
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Data analysis is of upmost importance in the mining of big data, where knowledge discovery and inference are the basis for intelligent systems to support the real world applications. However, the process involves knowledge acquisition, representation, inference and data, Bayesian network (BN) is the key technology plays a key role in knowledge representation, in order to pave way to cope with incomplete, fuzzy data to solve the real-life problems. This book presents Bayesian network as a technology to support data-intensive and incremental learning in knowledge discovery, inference and data fusion in uncertain environment. Contents: IntroductionData-Intensive Learning of Uncertain KnowledgeData-Intensive Inferences of Large-Scale Bayesian NetworksUncertain Knowledge Representation and Inference for Lineage Processing over Uncertain DataUncertain Knowledge Representation and Inference for Tracing Errors in Uncertain DataFusing Uncertain Knowledge in Time-Series DataSummary Readership: Graduate students, researchers and professionals in the field of artificial intelligence/machine learning and information sciences, especially in databases. Keywords: Uncertain Knowledge;Bayesian Network;Data-Intensive Computing;Lineage;Inference;FusionReview: Key Features: Upon the preliminaries of BN (Pearl, 1988), this book establishes the connection between massive/uncertain/dynamic data management and uncertainty in artificial intelligence, specifically taking BN as the knowledge framework; different from the publications (Pearl, 1988; Russel & Norvig, 2010), this book concerns uncertain knowledge representation and corresponding inferences from the data-driven perspective, where we focus on the construction of knowledge models with respect to specific applications; different from the publication (Han, 2011), this book focuses on the critical problem of knowledge engineering specially taking BN as the framework, instead of the previously-unknown patterns by mining dataThis book presents the theoretic conclusions, algorithmic strategies, running examples and empirical studies while emphasizing the soundness in both theoretic/semantic and executive/applicable perspectives of the methods for discovery and fusion of uncertain knowledge in dataThis book is appropriately a reference book for researchers in the fields of massive data analysis, artificial intelligence and knowledge engineering. As well, this book can be also adopted as textbook for graduate students who major in data mining and knowledge discovery, or intelligent data analysis etc.

Considerations on Representing and Handling Human Common sense Knowledge

Author : Neli Zlatareva
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Artificial Intelligence

Author : Ela Kumar
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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.

Uncertainty in Artificial Intelligence

Author : Didier J. Dubois
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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.

Foundations of Neural Networks Fuzzy Systems and Knowledge Engineering

Author : Nikola K. Kasabov
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Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

Quantified Representation of Uncertainty and Imprecision

Author : Dov M. Gabbay
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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.

Advanced Methods for Inconsistent Knowledge Management

Author : Ngoc Thanh Nguyen
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This book is a first. It fills a major gap in the market and provides a wide snapshot of intelligent technologies for inconsistency resolution. The need for this resolution of knowledge inconsistency arises in many practical applications of computer systems. This kind of inconsistency results from the use of various resources of knowledge in realizing practical tasks. These resources are often autonomous and use different mechanisms for processing knowledge about the same real world. This can lead to compatibility problems.

Scalable Uncertainty Management

Author : Salem Benferhat
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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.