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Books > Computing & IT > Applications of computing > Artificial intelligence
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Bayesian Nets and Causality: Philosophical and Computational Foundations (Hardcover)
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Bayesian Nets and Causality: Philosophical and Computational Foundations (Hardcover)
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Bayesian nets are widely used in artificial intelligence as a
calculus for casual reasoning, enabling machines to make
predictions, perform diagnoses, take decisions and even to discover
casual relationships. But many philosophers have criticized and
ultimately rejected the central assumption on which such work is
based-the causal Markov Condition. So should Bayesian nets be
abandoned? What explains their success in artificial intelligence?
This book argues that the Causal Markov Condition holds as a
default rule: it often holds but may need to be repealed in the
face of counter examples. Thus, Bayesian nets are the right tool to
use by default but naively applying them can lead to problems. The
book develops a systematic account of causal reasoning and shows
how Bayesian nets can be coherently employed to automate the
reasoning processes of an artificial agent. The resulting framework
for causal reasoning involves not only new algorithms, but also new
conceptual foundations. Probability and causality are treated as
mental notions - part of an agent's belief state. Yet probability
and causality are also objective - different agents with the same
background knowledge ought to adopt the same or similar
probabilistic and causal beliefs. This book, aimed at researchers
and graduate students in computer science, mathematics and
philosophy, provides a general introduction to these philosophical
views as well as exposition of the computational techniques that
they motivate.
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