Argumentation-Based Reasoning in BDI Agents Using Toulmin's Model

Abstract

The theory of argumentation spans several fields of knowledge, gaining significant space in the community of multiagent systems because it gives support for agents to reason about uncertain beliefs. This work describes the development of an argumentation-based inference architecture for BDI agents, which was developed based on Toulmin's model of argumentation. The philosopher Stephen Toulmin claimed that arguments typically consist of six parts: data, warrants, claim, backing, qualifiers, and rebuttals. Using the proposed architecture, an agent is able to create new beliefs based on available evidence and to justify such beliefs.