Möbus, Claus and Garbe, Hilke (2009) Learning the DAG of Bayesian belief networks by asking (conditional) (in-)dependence questions. In: K-CAP '09 Proceedings of the Fifth International Conference on Knowledge Capture. ACM Special Interest Group on Artificial Intelligence . Association for Computing Machinery, New York, NY, pp. 199-200. ISBN 978-1-60558-658-8

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Official URL: http://dl.acm.org/citation.cfm?doid=1597735.159778...


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.

Item Type: Book Section
Subjects: Generalities, computers, information > Computer science, internet
Philosophy and psychology > Psychology
Technology, medicine, applied sciences > Engineering and machine engineering
Divisions: School of Computing Science, Business Administration, Economics and Law > Department of Computing Science
Date Deposited: 09 Apr 2014 08:31
Last Modified: 09 Apr 2014 08:31
URI: https://oops.uni-oldenburg.de/id/eprint/1832
URN: urn:nbn:de:gbv:715-oops-19130
DOI: 10.1145/1597735.1597781

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