A Logical Theory about Dynamics in Abstract Argumentation

In Dung-style argumentation [3] an argumentation framework (AF for short) is usually assumed to be static. There are, however, many scenarios where argumentation is a dynamic process: Agents may learn that an AF must have a certain outcome and may learn about new arguments/attacks. We address these issues by answering the following research questions: How can we model an agent’s belief about the outcome of an AF? and How can we characterize the effects of an agent learning that the AF should have a certain outcome, or learning about new arguments/attacks? The basis of our approach is a logical labeling language, interpreted by labelings that assign to each argument a label indicating whether it is accepted, rejected or undecided [2]. Formulas in this language are statements about the acceptance of the arguments of an AF. This allows us to reason about the outcome of an AF in terms of beliefs, rather than extensions or labelings.