Abstract
The theory of argumentation pervades several fields of knowledge, and it has gained significant space in multiagent systems because it provides a way for modeling reasoning over conflicting information in intelligent agents. This work proposes the development of an argumentation-based inference mechanism for BDI agents based on Toulmin's model of argumentation. The philosopher Stephen Toulmin claimed that arguments typically consist of six parts: data, warrant, claim, backing, qualifier, and rebuttal. This argumentation structure allows arguments to be described through separated components, making it easier to define and to evaluate the inference process. By presenting and discussing some case studies, this paper shows how this mechanism supports the inference of new beliefs based on available evidence within BDI agents programmed in an agent-oriented programming language.
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