A Probabilistic Calculus of Actions
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(yX = x), which represents the observation X = x, and causal conditioning, P(ydo(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.
 Publication:

arXiv eprints
 Pub Date:
 February 2013
 arXiv:
 arXiv:1302.6835
 Bibcode:
 2013arXiv1302.6835P
 Keywords:

 Computer Science  Artificial Intelligence
 EPrint:
 Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)