Explaining Bayesian Networks using Argumentation

Sjoerd Timmer, John-Jules Meyer, Henry Prakken, Silja Renooij, Bart Verheij

Qualitative and quantitative systems to deal with uncer- tainty coexist. Bayesian networks are a well known tool in probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated they may appear as black box models. Ar- gumentation approaches, on the contrary, emphasise the derivation of results. Argumentation models, however, have notorious difficulty dealing with probabilities. In this paper we formalise a two-phase method to extract probabilistically supported arguments from a Bayesian network. First, from a BN we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the evidence modelled in the Bayesian network.

Manuscript (in PDF-format)

Reference:
Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). Explaining Bayesian Networks using Argumentation. Symbolic and Quantitative Approaches to Reasoning with Uncertainty 13th European Conference, ECSQARU 2015, Compi├Ęgne, France, July 15-17, 2015. Proceedings (eds. Destercke, S., & Denoeux, T.), 83-92. Berlin: Springer.


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