The problem of driving an autonomous vehicle in normal traffic engages many areas of AI
research and has substantial economic significance. We describe work in progress on a …

Bayesian decision analysis supports principled decision making in complex domains. This
textbook takes the reader from a formal analysis of simple decision problems to a careful …

This book presents four contributions to planning research within an integrated framework.
James Allen offers a survey of his research in the field of temporal reasoning, and then …

This paper describes the Graphical Models Toolkit (GMTK), an open source, publically
available toolkit for developing graphical-model based speech recognition and general time …

Relational Markov models (RMMs) are a generalization of Markov models where states can
be of different types, with each type described by a different set of variables. The domain of …

Facial expression is a natural and powerful means of human communication. Recognizing
spontaneous facial actions, however, is very challenging due to subtle facial deformation …

We define and implement a model of rational action for automated reasoning systems that
makes use of flexible approximation methods and inexpensive decision-theoretic …

The past few years have witnessed an significant interest in probabilistic logic learning, ie in
research lying at the intersection of probabilistic reasoning, logical representations, and …

Abstract We introduce the Piecewise-Constant Conditional Intensity Model, a model for
learning temporal dependencies in event streams. We describe a closed-form Bayesian …

This article discusses the foundations of the use of graphical models for speech recognition
as presented in JR Deller et al.(1993), XD Huang et al.(2001), F. Jelinek (19970, LR Rabiner …