Attack-Defense Semantics of Argumentation

Abstract

Abstract argumentation is an important research area in AI. It is mainly about the acceptability of arguments in an argumentation framework. The classical notion of defense has not fully reflected some useful information implicitly encoded by the interaction relation between arguments. In this paper, instead of using arguments and attacks as first citizens, a novel notion of attack-defense is adopted as a first citizen, based on which a theory of attack-defense framework and attackdefense semantics are established, where an attack-defense is a triple (x, y,z), meaning that: an argument x defends an argument z against an attacker y. Attack-defense semantics can be used not only to identify the impact of arguments in some odd cycles, and remove some “useless” defenses, but also to capture new types of equivalence that cannot be represented by the existing notions of equivalence of argumentation frameworks. In addition, it shows that an attack-defense framework and attack-defense semantics can represent some knowledge that cannot be represented in Dung-style argumentation, e.g., some context-sensitive knowledge in a dialogue.