Abstract
Argumentation as metaphor for logic programming semantics is a sound basis to define negotiating agents. If such agents operate in an open system, they have to be able to negotiate and argue efficiently in a goal-directed fashion and they have to deal with uncertain and vague knowledge. In this paper, we define an argumentation framework with fuzzy unification and reasoning for the well-founded semantics to handle uncertainty. In particular, we address three main problems: how to define a goal-directed top-down proof procedure for justified arguments, which is important for agents which have to respond in real-time; how to provide expressive knowledge representation including default and explicit negation and uncertainty, which is among others part of agent communication languages such as FIPA or KQML; how to deal with reasoning in open agent systems, where agents should be able to reason despite misunderstandings.
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