Hunter, A; Polberg, S; Potyka, N; (2018) Updating Belief in Arguments in Epistemic Graphs. In: Proceedings of the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). (pp. pp. 138-147). Association for the Advancement of Artificial Intelligence: Tempe, AZ, USA.

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Abstract

Epistemic graphs are a recent generalization of epistemic probabilistic argumentation. Relations between arguments can be supporting, attacking, as well as neither supporting nor attacking. These interdependencies are represented by epistemic constraints, and the semantics of epistemic graphs are given in terms of probability distributions satisfying these constraints. We investigate the behaviour of epistemic graphs in a dynamic setting where a given distribution can be updated once new constraints are presented. Our focus is on update methods that minimize the change in probabilistic beliefs. We show that all methods satisfy basic commonsense postulates, identify fragments of the epistemic constraint language that guarantee the existence of well-defined solutions, and explain how the problems that arise in more expressive fragments can be treated either automatically or by user support. We demonstrate the usefulness of our proposal by considering its application in computational persuasion.

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