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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 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.
Scalable Uncertainty Management
This book constitutes the refereed proceedings of the Second International Conference on Scalable Uncertainty Management, SUM 2008, held in Naples, Italy, in Oktober 2008. The 27 revised full papers presented together with the extended abstracts of 3 invited talks/tutorials were carefully reviewed and selected from 42 submissions. The papers address artificial intelligence researchers, database researchers, and practitioners to demonstrate theoretical techniques required to manage the uncertainty that arises in large scale real world applications and to cope with large volumes of uncertainty and inconsistency in databases, the Web, the semantic Web, and artificial intelligence in general.
Symbolic and Quantitative Approaches to Reasoning and Uncertainty
This book constitutes the refereed proceedings of the 1999 European Conference on Symbolic and Quantitative Approaches to Reasoning under Uncertainty, ECSQARU'99, held in London, UK, in July 1999. The 35 revised full papers presented were carefully reviewed and selected for inclusion in the book by the program committee. The volume covers theoretical as well as application-oriented aspects of various formalisms for reasoning under uncertainty. Among the issues addressed are default reasoning, nonmonotonic reasoning, fuzzy logic, Bayesian theory, probabilistic reasoning, inductive learning, rough knowledge discovery, Dempster-Shafer theory, qualitative decision making, belief functions, and evidence theory.
Symbolic and Quantitative Approaches to Reasoning with Uncertainty
This book constitutes the refereed proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015, held in Compiègne, France, in July 2015. The 49 revised full papers presented were carefully reviewed and selected from 69 submissions and cover topics on decision theory and preferences; argumentation; conditionals; game theory; belief update; classification; inconsistency; graphical models; Bayesian networks; belief functions; logic; and probabilistic graphical models for scalable data analytics. Papers come from researchers interested in advancing the technology and from practitioners using uncertainty techniques in real-world applications. The scope of the ECSQARU conferences encompasses fundamental issues, representation, inference, learning, and decision making in qualitative and numeric uncertainty paradigms.
Scalable Uncertainty Management
This book constitutes the refereed proceedings of the 14th International Conference on Scalable Uncertainty Management, SUM 2020, which was held in Bozen-Bolzano, Italy, in September 2020. The 12 full, 7 short papers presented in this volume were carefully reviewed and selected from 30 submissions. Besides that, the book also contains 2 abstracts of invited talks, 2 tutorial papers, and 2 PhD track papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis. Due to the Corona pandemic SUM 2020 was held as an virtual event.
Soft Methods for Handling Variability and Imprecision
Probability theory has been the only well-founded theory of uncertainty for a long time. It was viewed either as a powerful tool for modelling random phenomena, or as a rational approach to the notion of degree of belief. During the last thirty years, in areas centered around decision theory, artificial intelligence and information processing, numerous approaches extending or orthogonal to the existing theory of probability and mathematical statistics have come to the front. The common feature of those attempts is to allow for softer or wider frameworks for taking into account the incompleteness or imprecision of information. Many of these approaches come down to blending interval or fuzzy i...
ECAI 2006
In the summer of 1956, John McCarthy organized the famous Dartmouth Conference which is now commonly viewed as the founding event for the field of Artificial Intelligence. During the last 50 years, AI has seen a tremendous development and is now a well-established scientific discipline all over the world. Also in Europe AI is in excellent shape, as witnessed by the large number of high quality papers in this publication. In comparison with ECAI 2004, there's a strong increase in the relative number of submissions from Distributed AI/Agents and Cognitive Modelling. Knowledge Representation & Reasoning is traditionally strong in Europe and remains the biggest area of ECAI 2006. One reason the figures for Case-Based Reasoning are rather low is that much of the high quality work in this area has found its way into prestigious applications and is thus represented under the heading of PAIS.
Uncertainty Modelling in Data Science
This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair. Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including ...
