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.
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Xiang, Y., Truong, M., Zhu, J., Stanley, D., Nonnecke, B. (2011). Indirect Elicitation of NIN-AND Trees in Causal Model Acquisition. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_21
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DOI: https://doi.org/10.1007/978-3-642-23963-2_21
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