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This paper focuses on approaches that address the intractability of knowledge acquisition of conditional probability tables in causal or Bayesian belief networks. We state a rule that we term the "recursive noisy OR" (RNOR) which allows combinations of dependent causes to be entered and later used for estimating the probability of an effect. In the(More)
This paper provides <i>a priori</i> cirteria for determing when a causal model is sufficiently complete to be considered a Bayesian Network, and a new representation for Bayesian Networks shown to be more computationally efficient in a wide range of circumstances than current representations.Expert Systems for domains in which uncertainty plays a major role(More)
This paper describes a new technique for interactive planning for coalition operations under conditions of uncertainty. Our approach is based on the use of the Air Force Research Laboratory's Causal Analysis Tool (CAT), a system for creating and analyzing causal models similar to Bayesian networks. Interactive course-of-action planning using causal models.(More)
Causal Models are like Dependency Graphs and Belief Nets in that they provide a structure and a set of assumptions from which a joint distribution can, in principle, be computed. Unlike Dependency Graphs, Causal Models are models of hierarchical and/or parallel processes, rather than models of distributions (partially) known to a model builder through some(More)
This paper describes a new technique for interactive planning under conditions of uncertainty. Our approach is based on the use of the Air Force Research Laboratory's Causal Analysis Tool (CAT), a system for creating and analyzing causal models similar to Bayes networks. In order to use CAT as a tool for planning, users go through an iterative process in(More)
diagnosis using a reformulation of the internist-1/qmr knowledge base: The probabilistic model and inference algorithms. 12 In the next step, the select-cluster procedure requires the probabilities shown in the second row of the table. We observe that Pr(:v 2 j v 4) need not be computed: from the independencies of the digraph of the belief network we have(More)