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Reasoning about Uncertainty

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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 t 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.


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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 t 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.

41 review for Reasoning about Uncertainty

  1. 4 out of 5

    Lurino

    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 expansi 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.

  2. 5 out of 5

    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! 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!

  3. 5 out of 5

    TK Keanini

    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.

  4. 5 out of 5

    Henrik Åslund

  5. 5 out of 5

    Bob

  6. 4 out of 5

    Keith Mcgreggor

  7. 4 out of 5

    Talal Alrawajfeh

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    Rajesh

  9. 5 out of 5

    Steve

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    Samuel Krasnik

  11. 4 out of 5

    Carmine

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    Naruaki

  13. 5 out of 5

    Robert

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    John Ackley

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    Tim

  16. 4 out of 5

    John

  17. 5 out of 5

    mcburton

  18. 4 out of 5

    Morris Morris

  19. 5 out of 5

    Joseph

  20. 5 out of 5

    Peter Baker

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    Hayley

  22. 4 out of 5

    Ken

  23. 5 out of 5

    Xuan Vinh Doan

  24. 5 out of 5

    Jeremy Trouncer

  25. 5 out of 5

    Nick

  26. 4 out of 5

    flamingleg

  27. 4 out of 5

    Gregory

  28. 5 out of 5

    Kaiser

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    Brian

  30. 4 out of 5

    Darin

  31. 5 out of 5

    David Scarratt

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  33. 4 out of 5

    Robert Kosara

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    Brad

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    John Ledesma

  36. 4 out of 5

    Ash

  37. 5 out of 5

    Toryn Green

  38. 5 out of 5

    Voltariy Sarella

  39. 4 out of 5

    John

  40. 4 out of 5

    Wikimedia Italia

  41. 4 out of 5

    Xoanon93

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