Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision
science have common roots and strive for similar goals. This paper surveys the potential for …

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the
theoretical foundations and computational methods that underlie plausible reasoning under …

This paper presents and compares two views of the theory of rough sets. The operator-
oriented view interprets rough set theory as an extension of set theory with two additional …

Bayesian belief networks and influence diagrams are attractive approaches for representing
uncertain expert knowledge in coherent probabilistic form. But current algorithms for …

The theory of rough sets is an extension of set theory with two additional unary set-theoretic
operators defined based on a binary relation on the universe. These two operators are …

Counterfactuals are a form of common-sense nonmonotonic inference that has been of long-
term interest to philosophers. In this paper, we begin by describing some of the impact …

Publisher Summary The province of nonmonotonic reasoning is the derivation of plausible
but not infallible conclusions from a knowledge base viewed abstractly as a set of formulas …

Stochastic simulation is a method of computing probabilities by recording the fraction of time
that events occur in a random series of scenarios generated from some causal model. This …

The primary aim of this monograph is to provide a formal framework for the representation
and management of uncertainty and vagueness in the field of artificial intelligence. It puts …

In this book Simon Parsons describes qualitative methods for reasoning under uncertainty,"
uncertainty" being a catch-all term for various types of imperfect information. The advantage …