Abstract
Computational argumentation has taken a predominant place in the modeling of negotiation dialogues over the last years. A competent agent participating in a negotiation process is expected to decide its next move taking into account an, often incomplete, model of its opponent. This work provides a complete computational account of argumentation-based negotiation under incomplete opponent profiles. After the agent identifies its best option, in any state of a negotiation, it looks for suitable arguments that support this option in the theory of its opponent. As the knowledge on the opponent is uncertain, the challenge is to find arguments that, ideally, support the selected option despite the uncertainty. We present a negotiation framework based on these ideas, along with experimental evidence that highlights the advantages of our approach.
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