Reasoning About Uncertainty

by Joseph Y. Halpern

Uncertainty is a fundamental and unavoidable feature of daily life; in order to dealwith uncertaintly intelligently, we need to be able to represent it and reason about it. In thisbook, Joseph Halpern examines formal ways of representing uncertainty and considers various logicsfor reasoning about it. While the ideas presented are formalized in terms of definitions andtheorems, the emphasis is on the philosophy of representing and reasoning about uncertainty; thematerial is accessible and relevant to researchers and students in many fields, including computerscience, artificial intelligence, economics (particularly game theory), mathematics, philosophy, andstatistics.Halpern begins by surveying possible formal systems for representing uncertainty,including probability measures, possibility measures, and plausibility measures. He considers theupdating of beliefs based on changing information and the relation to Bayes' theorem; this leads toa discussion of qualitative, quantitative, and plausibilistic Bayesian networks. He considers notonly the uncertainty of a single agent but also uncertainty in a multi-agent framework. Halpern thenconsiders the formal logical systems for reasoning about uncertainty. He discusses knowledge andbelief; default reasoning and the semantics of default; reasoning about counterfactuals, andcombining probability and counterfactuals; belief revision; first-order modal logic; and statisticsand beliefs. He includes a series of exercises at the end of each chapter.

Genres: Philosophy, Mathematics, Logic, Science, Textbooks, Economics, Artificial Intelligence

456 pages, Hardcover

First published January 1, 2003

Reviews

[Lurino · Rating 5 out of 5]

I found this book as very useful for students and researchers in social science and humanities. The math is not too complex, in the sense that it's mostly a formalization of ideas and concepts, instead of proofs, and it provides lots of useful explanations for people who are trying to understand this complex reality, filled with many uncertainties.

However, if you're really against anything mathematical, i suppose you won't be able to enjoy the full richness of this book.

It starts with an expansive guide on how to represent uncertainties, beliefs about anything, an introduction to bayesian approach, how to expect anything, how to decide anything, and then it continues with a detailed explanations on all the reasoning processes necessary to understand complex reality.

[Nick Black]

Oooh, an exciting find among the bibliography of one of the best (and by far most broadly-sourced, an attribute CS research tends to sadly lack) papers I've read this month: Nain and Vardi's invited ATVA2007 effort "Branching Time vs Linear Time: Semantical Perspective" (stop for a second and go read this paper. Seriously, it's fantastic).

For what it's worth, I originally read about Nain+Vardi-2007 on λtU back in April, and just now got around to reading it...argh!

[TK Keanini · Rating 5 out of 5]

This is a great book and very well structured. There is a lot of math in it and if you are like me, you will need to grab a tutor of a friend to walk you through the heavy duty mathmatical terms.

The beauty of this book is that everything is presented to you for you to make the best decision on which method fits your fancy.

I expect to read this book a few times. I've already read the first two chapters twice and each time got something new out of it.