"Probabilistic Reasoning in Intelligent Systems" is a complete
and accessible account of the theoretical foundations and
computational methods that underlie plausible reasoning under
uncertainty. The author provides a coherent explication of
probability as a language for reasoning with partial belief and
offers a unifying perspective on other AI approaches to
uncertainty, such as the Dempster-Shafer formalism, truth
maintenance systems, and nonmonotonic logic.
The author distinguishes syntactic and semantic approaches to
uncertainty--and offers techniques, based on belief networks, that
provide a mechanism for making semantics-based systems operational.
Specifically, network-propagation techniques serve as a mechanism
for combining the theoretical coherence of probability theory with
modern demands of reasoning-systems technology: modular declarative
inputs, conceptually meaningful inferences, and parallel
distributed computation. Application areas include diagnosis,
forecasting, image interpretation, multi-sensor fusion, decision
support systems, plan recognition, planning, speech recognition--in
short, almost every task requiring that conclusions be drawn from
uncertain clues and incomplete information.
"Probabilistic Reasoning in Intelligent Systems" will be of
special interest to scholars and researchers in AI, decision
theory, statistics, logic, philosophy, cognitive psychology, and
the management sciences. Professionals in the areas of
knowledge-based systems, operations research, engineering, and
statistics will find theoretical and computational tools of
immediate practical use. The book can also be used as an excellent
text for graduate-level courses in AI, operations research, or
applied probability.
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