Synopses & Reviews
andlt;Pandgt;Uncertainty is a fundamental and unavoidable feature of daily life; in order to deal with uncertaintly 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; the material is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.Halpern begins by surveying possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures. He considers the updating of beliefs based on changing information and the relation to Bayes' theorem; this leads to a discussion of qualitative, quantitative, and plausibilistic Bayesian networks. He considers not only the uncertainty of a single agent but also uncertainty in a multi-agent framework. Halpern then considers the formal logical systems for reasoning about uncertainty. He discusses knowledge and belief; default reasoning and the semantics of default; reasoning about counterfactuals, and combining probability and counterfactuals; belief revision; first-order modal logic; and statistics and beliefs. He includes a series of exercises at the end of each chapter.andlt;/Pandgt;
Review
Uncertainty is a central topic in many domains, such as economics, logic, artificial intelligence, and statistics. It takes an omniscientist such as Joe Halpern to treat this topic in full. His book is a rich source of unique insights, offering unexpected connections between different fields. Bas C. van Fraassen, Department of Philosophy, Princeton University
Review
"Halpern presents a masterful, complete and unified account of the many ways in which the connections between logic, probability theory and commonsensical linguistic terms can be formalized. Terms such as 'true,' 'certain,' 'plausible,' 'possible,' 'believed,' 'known,' 'default,' 'relevant,' 'independent,' and 'preferred are given rigorous semantical and syntactical analyses, and their interrelationships explicated and exemplified. An authoritative panoramic reference for philosophers, cognitive scientists and artificial intelligence researchers."--Judea Pearl, Computer Science Department, University of California, Los Angeles
Review
Reasoning about Uncertainty is a very valuable synthesis of the mathematics of uncertainty as it has developed in a number of related fields -- probability, statistics, computer science, game theory, artificial intelligence, and philosophy. Researchers in all of these fields will find this a very useful book -- both for its elegant treatment of technical results and for its illuminating conceptual discussions. Wolfgang Spohn, University of Konstanz
Review
Reasoning about Uncertainty pursues its own unified theoretical perspective in a remarkably systematic way; yet it is also a remarkably rich and complete textbook. It will be a rewarding book to work through for students and researchers alike. Judea Pearl, Computer Science Department, University of California, Los Angeles
Review
andquot;Reasoning About Uncertainty pursues its own unified theoretical perspective in a remarkably systematic way; yet it is also a remarkably rich and complete textbook. It will be a rewarding book to work through for students and researchers alike.andquot;
andmdash;Wolfgang Spohn, University of Konstanz
Review
For more than a decade, the study of uncertain reasoning has been graced by the breadth, openness, and agility of Joe Halpern's intellect. More than any of his colleagues, Joe has sought to reconcile and unify the diverse insights and methods for reasoning about knowledge and uncertainty that have been developed and championed in various academic fields. This cheerful, measured, and comprehensive book will bring Joe's tone, as well as his individual contributions, to the forefront of the field. I cannot imagine a better starting place for a student of the subject. The MIT Press
Review
For some years now I have been testing a hypothesis: if a topic involving probability is of current interest to a philosopher, then Joseph Halpern has proved an important result that is relevant to it. Its accuracy can be gauged by the frequency with which I recommend his papers to colleagues and students. This book, which presents all these valuable contributions in a single volume, provides a rich source of technical and philosophical insight. Glenn Shafer, Department of Accounting and Information Systems, Rutgers University School of Business
Review
Reiter's new book, Knowledge in Action, offers the first systematic account of the logical approach to cognitive robotics, a field that he and his colleagues have developed over the past decade. The unique feature of this approach rests in its capacity to admit specifications in the form of meaningful knowledge fragments, to piece those fragments together by logical and probabilistic inferences, and to use those inferences to guide both manipulative and perceptual actions by programmable agents. A must for anyone concerned with the foundations of common sense knowledge or the design of autonomous dynamical systems. Peter P. Wakker, Department of Economics, University of Amsterdam
Review
andlt;Pandgt;"For more than a decade, the study of uncertain reasoning has been graced by the breadth, openness, and agility of Joe Halpern's intellect. More than any of his colleagues, Joe has sought to reconcile and unify the diverse insights and methods for reasoning about knowledge and uncertainty that have been developed and championed in various academic fields. This cheerful, measured, and comprehensive book will bring Joe's tone, as well as his individual contributions, to the forefront of the field. I cannot imagine a better starting place for a student of the subject."--Glenn Shafer, Department of Accounting and Information Systems, Rutgers University School of Businessandlt;/Pandgt; The MIT Press
Review
andlt;Pandgt;"Reiter's new book, Knowledge in Action, offers the first systematic account of the logical approach to cognitive robotics, a field that he and his colleagues have developed over the past decade. The unique feature of this approach rests in its capacity to admit specifications in the form of meaningful knowledge fragments, to piece those fragments together by logical and probabilistic inferences, and to use those inferences to guide both manipulative and perceptual actions by programmable agents. A must for anyone concerned with the foundations of commonsense knowledge or the design of autonomous dynamical systems."--Judea Pearl, Computer Science Department, University of California, Los Angelesandlt;/Pandgt; The MIT Press The MIT Press
Review
andlt;Pandgt;" andlt;Iandgt;Reasoning about Uncertaintyandlt;/Iandgt; is a very valuable synthesis of the mathematics of uncertainty as it has developed in a number of related fieldsand#38;mdash;probability, statistics, computer science, game theory, artificial intelligence, and philosophy. Researchers in all of these fields will find this a very useful bookand#38;mdash;both for its elegant treatment of technical results and for its illuminating conceptual discussions." Adam Brandenburger, J.P. Valles Professor of Business Economics and Strategy, Stern School of Business, New York Universityandlt;/Pandgt; The MIT Press
Review
andlt;Pandgt;"Reasoning About Uncertainty pursues its own unified theoretical perspective in a remarkably systematic way; yet it is also a remarkably rich and complete textbook. It will be a rewarding book to work through for students and researchers alike." Wolfgang Spohn, University of Konstanzandlt;/Pandgt; The MIT Press
Synopsis
Using formal systems to represent and reason about uncertainty.
Synopsis
Uncertainty is a fundamental and unavoidable feature of daily life; in order to deal with uncertaintly 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; the material is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.
Halpern begins by surveying possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures. He considers the updating of beliefs based on changing information and the relation to Bayes' theorem; this leads to a discussion of qualitative, quantitative, and plausibilistic Bayesian networks. He considers not only the uncertainty of a single agent but also uncertainty in a multi-agent framework. Halpern then considers the formal logical systems for reasoning about uncertainty. He discusses knowledge and belief; default reasoning and the semantics of default; reasoning about counterfactuals, and combining probability and counterfactuals; belief revision; first-order modal logic; and statistics and beliefs. He includes a series of exercises at the end of each chapter.
Synopsis
Uncertainty is a fundamental and unavoidable feature of daily life; in order to deal with uncertaintly 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; the material is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.
About the Author
Joseph Y. Halpern is Professor of Computer Science at Cornell University.