Reasoning about Uncertainty

by Joseph Y. Halpern

Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics.In order to deal with uncertainty 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. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks.

This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security.
Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.

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

504 pages, Paperback

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.