Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

This book provides a comprehensive and much needed introduction to the field by one of its
foremost experts. It is beautifully written and presents a unifying framework capturing a wide …

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a
general model for decision-making under uncertainty in decentralized settings, but are …

Real-time problem diagnosis in large distributed computer systems and networks is a
challenging task that requires fast and accurate inferences from potentially huge data …

Graphical models (eg, Bayesian and constraint networks, influence diagrams, and Markov
decision processes) have become a central paradigm for knowledge representation and …

Proteins are chains of simple molecules called amino acids. The three-dimensional shape of
a protein and its amino acid composition define its biological function. Over millions of years …

A Bayesian network methodology is developed for performing infrastructure seismic risk
assessment and providing decision support with an emphasis on immediate post …

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the
last twenty years or so, both the Database community and the AI community have studied …

This is the first of two papers presenting and evaluating the power of a new framework for
combinatorial optimization in graphical models, based on AND/OR search spaces. We …

Previous algorithms for unrestricted constraint satisfaction use reduction search, which
inferentially removes values from domains in order to prune the backtrack search tree. This …