We review the use of factor graphs for the modeling and solving of large-scale inference
problems in robotics. Factor graphs are a family of probabilistic graphical models, other …

Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

Computational Intelligence comprises concepts, paradigms, algorithms, and
implementations of systems that are supposed to exhibit intelligent behavior in complex …

This paper presents a tutorial introduction to the use of variational methods for inference and
learning in graphical models (Bayesian networks and Markov random fields). We present a …

Probabilistic graphical models and decision graphs are powerful modeling tools for
reasoning and decision making under uncertainty. As modeling languages they allow a …

Intelligent agents are employed as the central characters in this new introductory text.
Beginning with elementary reactive agents, Nilsson gradually increases their cognitive …

Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

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 …

Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that
support the modelling of uncertainty and decisions in large complex domains, while …