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Advances in Bayesian Networks
In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
Handbook of Defeasible Reasoning and Uncertainty Management Systems
Reasoning under uncertainty is always based on a specified language or for malism, including its particular syntax and semantics, but also on its associated inference mechanism. In the present volume of the handbook the last aspect, the algorithmic aspects of uncertainty calculi are presented. Theory has suffi ciently advanced to unfold some generally applicable fundamental structures and methods. On the other hand, particular features of specific formalisms and ap proaches to uncertainty of course still influence strongly the computational meth ods to be used. Both general as well as specific methods are included in this volume. Broadly speaking, symbolic or logical approaches to uncertaint...
Symbolic and Quantitative Approaches to Reasoning and Uncertainty
In recent years it has become apparent that an important part of the theory of artificial intelligence is concerned with reasoning on the basis of uncertain, incomplete, or inconsistent information. A variety of formalisms have been developed, including nonmonotonic logic, fuzzy sets, possibility theory, belief functions, and dynamic models of reasoning such as belief revision and Bayesian networks. Several European research projects have been formed in the area and the first European conference was held in 1991. This volume contains the papers accepted for presentation at ECSQARU-93, the European Conference on Symbolicand Quantitative Approaches to Reasoning and Uncertainty, held at the University of Granada, Spain, November 8-10, 1993.
Belief Change
Belief change is an emerging field of artificial intelligence and information science dedicated to the dynamics of information and the present book provides a state-of-the-art picture of its formal foundations. It deals with the addition, deletion and combination of pieces of information and, more generally, with the revision, updating and fusion of knowledge bases. The book offers an extensive coverage of, and seeks to reconcile, two traditions in the kinematics of belief that often ignore each other - the symbolic and the numerical (often probabilistic) approaches. Moreover, the work encompasses both revision and fusion problems, even though these two are also commonly investigated by diff...
Scalable Uncertainty Management
This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017. The 24 full and 6 short papers presented in this volume were carefully reviewed and selected from 35 submissions. The book also contains 3 invited papers. Managing uncertainty and inconsistency has been extensively explored in Artificial Intelligence over a number of years. Now, with the advent of massive amounts of data and knowledge from distributed, heterogeneous, and potentially conflicting sources, there is interest in developing and applying formalisms for uncertainty and inconsistency in systems that need to better manage this data and knowledge. The International Conference on Scalable Uncertainty (SUM) aims to provide a forum for researchers who are working on uncertainty management, in different communities and with different uncertainty models, to meet and exchange ideas.
Scalable Uncertainty Management
This book constitutes the refereed proceedings of the First International Conference on Scalable Uncertainty Management, SUM 2007, held in Washington, DC, USA, in October 2007. The 20 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers address artificial intelligence researchers, database researchers and practitioners.
Information Algebras
Information usually comes in pieces, from different sources. It refers to different, but related questions. Therefore information needs to be aggregated and focused onto the relevant questions. Considering combination and focusing of information as the relevant operations leads to a generic algebraic structure for information. This book introduces and studies information from this algebraic point of view. Algebras of information provide the necessary abstract framework for generic inference procedures. They allow the application of these procedures to a large variety of different formalisms for representing information. At the same time they permit a generic study of conditional independence, a property considered as fundamental for knowledge presentation. Information algebras provide a natural framework to define and study uncertain information. Uncertain information is represented by random variables that naturally form information algebras. This theory also relates to probabilistic assumption-based reasoning in information systems and is the basis for the belief functions in the Dempster-Shafer theory of evidence.
Uncertainty in Artificial Intelligence
Uncertainty Proceedings 1994
