Philippe Smets – författare

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.

This volume deals with approaches to handling contradictory information. These include approaches for actual contradiction - both A and not A can be proven from the information - and approaches for potential contradiction - where the information may contain arguments for A and arguments for not A, but the system suppresses the contradiction by, for example, preferring some arguments over others. Approaches covered include paraconsistent logics, modal logics, default logics, conditional logics, defeasible logics and paraconsistent semantics for logic programming. The volume should be of interest to students, researchers and practitioners in artificial intelligence, software engineering, logic, language and philosophy.

Belief change is an emerging field of artificial intelligence and information science dedicated to the dynamics of information, and this text provides a 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 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 different communities. Finally, the book presents the numerical view of belief change, beyond the probabilistic framework, covering such approaches as possibility theory, belief functions and convex gambles. The work thus presents a unified view of belief change operators, drawing from a widely scattered literature embracing philosophical logic, artificial intelligence, uncertainty modelling and database systems.The material constitutes a guide to the literature on the dynamics of epistemic states, knowledge bases and uncertain information.

This book contains leading survey papers on the various aspects of abduction, both logical and numerical approaches. Abduction is central to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision theory, as well as logic itself.

This handbook surveys the computational and algorithmic problems of systems of applied reasoning. The various theoretical and modelling aspects of defeasible reasoning were dealt with in the first four volumes, and this volume now turns to the algorithmic aspect. Topics covered include: Computation in valuation algebras; consequence finding algorithms; possibilistic logic; probabilistic argumentation systems, networks and satisfiability; algorithms for imprecise probabilities, for Dempster-Shafer, and network based decisions.

This is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modelling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. The first part of the text describes issues and challenges in the area of imperfect information that confront information systems, and the second part covers the principal theories for modelling imperfect information, and shows how these theories may be adapted to information systems. All chapters present solutions that have been applied, and the experiences that have been gained from those solutions. The material has been closely edited by the book's editors for content, consistency and style.

As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertainty modeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view­ points of information systems experts on the needs that challenge the uncer­ tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs.

This first volume is concerned with the Quantified Representation of Uncertainty and Imprecision. The 14 chapters cover most of the theories that have been proposed for that task and focus on the meaning of those theories. The book gives wide coverage to new theories of quantified uncertainty, as surveys of such theories are difficult to find. This handbook is probably the first to present together so many different theories of quantified uncertainty.

This volume deals with approaches to handling contradictory information. These include approaches for actual contradiction - both A and not-A can be proven from the information - and approaches for potential contradiction - where the information may contain arguments for A and arguments for not-A, but the system suppresses the contradiction by, for example, preferring some arguments over others. Approaches covered include paraconsistent logics, modal logics, default logics, conditional logics, defeasible logics and paraconsistent semantics for logic programming. The volume is of interest to students, researchers and practitioners in artificial intelligence, software engineering, logic, language and philosophy. This volume is the first handbook to give a comprehensive coverage of handling contradictory information.

This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches. Abduction is central to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision theory, as well as logic itself.

The Handbook of Defeasible Reasoning and Uncertainty Management Systems is unique in its masterly survey of the computational and algorithmic problems of systems of applied reasoning. The various theoretical and modelling aspects of defeasible reasoning were dealt with in the first four volumes, and Volume 5 now turns to the algorithmic aspect. Topics covered include: Computation in valuation algebras; consequence finding algorithms; possibilistic logic; probabilistic argumentation systems, networks and satisfiability; algorithms for imprecise probabilities, for Dempster-Shafer, and network based decisions.