Probabilistic Argumentation with Epistemic Extensions and Incomplete Information
This work addresses the challenge of handling incomplete information in argumentation systems for AI and reasoning communities, offering an incremental enhancement to existing probabilistic argumentation methods.
The paper tackles the problem of representing and evaluating arguments under uncertainty by introducing probabilistic assignments to arguments, interpreted as an agent's belief in their justifiability, and defines epistemic extensions based on these probabilities. The result is a framework that extends standard argumentation extensions with new kinds derived from probabilistic constraints.
Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by a probability assignment to each argument. There are various interpretations that can be ascribed to this assignment. In this paper, we regard the assignment as denoting the belief that an agent has that an argument is justifiable, i.e., that both the premises of the argument and the derivation of the claim of the argument from its premises are valid. This leads to the notion of an epistemic extension which is the subset of the arguments in the graph that are believed to some degree (which we defined as the arguments that have a probability assignment greater than 0.5). We consider various constraints on the probability assignment. Some constraints correspond to standard notions of extensions, such as grounded or stable extensions, and some constraints give us new kinds of extensions.