Abstract
When data are insufficient to support learning, causal modeling, such as noisy-OR, aids elicitation by reducing probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider their interactions from the perspective of reinforcement or undermining. We show that none of them can represent both interactions. We present the first explicit causal model that can encode both reinforcement and undermining and we show how to use such a model to support efficient probability elicitation.
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Xiang, Y., Jia, N. (2006). Modeling Causal Reinforcement and Undermining with Noisy-AND Trees. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_15
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DOI: https://doi.org/10.1007/11766247_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34628-9
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