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

Integration of visual modules: an extension of the Marr paradigm | Guide books skip to main
content ACM Digital Library home ACM corporate logo Google, Inc. (search) Advanced Search …

An important issue faced by contemporary artificial intelligence workers is how to deal with
uncertain information. Four of the more prominent calculi-probability theory (especially the …

Methods to aggregate convex sets of probabilities are proposed. Source reliability is taken
into account by transforming the given information and making it less precise. An important …

Knowledge bases: Integration, and partitioning verification, Page 1 European Journal of
Operational Research 42 (1989) 111-128 111 North-Holland Invited Review Knowledge bases …

Uncertainty is of paramount importance in artificial intelligence, applied science, and many
other areas of human endeavour. Whilst each and every one of us possesses some intuitive …

This study presents two different perspectives of the Dempster-Shafer theory ofbelief
functions. The first view of the theory, called the compatibility view, constructs the belief …

The categorial approach to evidential reasoning can be seen as a combination of the
probability kinematics approach of Richard Jeffrey (1965) and the maximum (cross-) entropy …

This paper presents a bibliography of over 1400 references related to computer vision and
image analysis, arranged by subject matter. The topics covered include architectures; …

Reasoning with uncertainty is a field with many different approaches and viewpoints, with
important applications to sensor design and autonomous system development. It is …