Structured argumentation involves drawing inferences from knowledge in order to construct arguments and counterarguments. Since knowledge can be uncertain, we can use a probabilistic approach to representing and reasoning with the knowledge. Individual arguments can be constructed from the knowledge, with the belief in each argument determined just from the belief in the formulae appearing in the argument. However, if the original knowledgebase is inconsistent, this does not take into account the counterarguments that can be constructed. We therefore need a wider perspective that revises the belief in individual arguments in order to take into account the counterarguments. To address this need, we present a framework for probabilistic argumentation that uses relaxation methods to give a coherent view on the knowledge, and thereby revises the belief in the arguments that are generated from the knowledge.
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