Uncertainty in Artificial Intelligence
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A Probabilistic Calculus of Actions
Judea Pearl
Abstract:
We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which represents the observation X = x, and causal conditioning, P(y|do(X = x)), read the probability of Y = y conditioned on holding X constant (at x ) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions (and policies) from partially specified knowledge bases, such as Bayesian networks in which some conditional probabilities may not be available.
Keywords: Actions, causality, Bayesian networks.
Pages: 454-462
PS Link: ftp://ftp.cs.ucla.edu/pub/stat_ser/WWW/r212.ps.Z
PDF Link: /papers/94/p454-pearl.pdf
BibTex:
@INPROCEEDINGS{Pearl94,
AUTHOR = "Judea Pearl ",
TITLE = "A Probabilistic Calculus of Actions",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "454--462"
}


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