Indirect Elicitation of NIN-AND Trees in Causal Model Acquisition

  • Yang Xiang
  • Minh Truong
  • Jingyu Zhu
  • David Stanley
  • Blair Nonnecke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6929)

Abstract

To specify a Bayes net, a conditional probability table, often of an effect conditioned on its n causes, needs to be assessed for each node. Its complexity is generally exponential in n and hence how to scale up is important to knowledge engineering. The non-impeding noisy-AND (NIN-AND) tree causal model reduces the complexity to linear while explicitly expressing both reinforcing and undermining interactions among causes. The key challenge to acquisition of such a model from an expert is the elicitation of the NIN-AND tree topology. In this work, we propose and empirically evaluate two methods that indirectly acquire the tree topology through a small subset of elicited multi-causal probabilities. We demonstrate the effectiveness of the methods in both human-based experiments and simulation-based studies.

Keywords

True Model Tree Topology Causal Model Causal Interaction Conditional Probability Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yang Xiang
    • 1
  • Minh Truong
    • 1
  • Jingyu Zhu
    • 1
  • David Stanley
    • 1
  • Blair Nonnecke
    • 1
  1. 1.University of GuelphCanada

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