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The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability. It is a flexible' and unifying tool suited for handling, e.g., partial probability assessments (not requiring that the set of all possible outcomes' be endowed with a previously given algebraic structure, such as a Boolean algebra), and conditional independence, in a...

The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability. It is a flexible' and unifying tool suited for handling, e.g., partial probability assessments (not requiring that the set of all possible outcomes' be endowed with a previously given algebraic structure, such as a Boolean algebra), and conditional independence, in a...

Knowledge acquisition is one of the most important aspects influencing the quality of methods used in artificial intelligence and the reliability of expert systems. The various issues dealt with in this volume concern many different approaches to the handling of partial knowledge and to the ensuing methods for reasoning and decision making under uncertainty, as applied to problems in artificial intelligence. The volume is composed of the invited and contributed papers presented at the Workshop on Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, held at the Ettore...