Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A greedy knowledge acquisition method for the rapid prototyping of knowledge structures
Proceedings of the 3rd international conference on Knowledge capture
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A Greedy Knowledge Acquisition Method for the Rapid Prototyping of Bayesian Belief Networks
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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Bayesian belief networks (BBNs) have become the de facto standard for the representation of uncertain knowledge. They consist of a qualitative and of a quantitative part describing the (in-)dependencies between the variables of interest as a directed acyclic graph (DAG) and the decomposition of the joint probability distribution (JPD) as a product of conditional probability distributions constrained by the structure of the DAG. In this paper we present a new constraint-based query procedure: Query-an-Oracle (QAO). We assume that an oracle -- preferable a human domain expert -- is at hand which is competent and willing to answer questions generated by QAO concerning the directed (causal) dependence and (conditional) independence of the relevant random variables in the domain. Compared to other structure learning methods (e.g. the PC-Algorithm of Peter Spirtes and Clark Glymour and the IC-Algorithm of Pearl) QAO has a number of advantages. It derives the DAG of the BBN with less computational complexity, with no redundant questions, and is able to exploit directed dependence information without urging oracles to differentiate between direct and indirect influence.