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.

在过去的几年中，计算论证在谈判对话的建模中占据了主导地位。参加谈判过程的胜任代理人应考虑其对手的模型（通常是不完整的）来决定下一步行动。这项工作在不完整的对手情况下提供了基于论证的谈判的完整计算方法。代理在协商的任何状态下确定其最佳选择后，便会在其对手的理论中寻找支持该选择的合适论点。由于关于对手的知识尚不确定，因此面临的挑战是寻找能够在理想情况下尽管不确定但仍支持所选选项的论点。我们提出了一个基于这些想法的谈判框架，以及突出了我们方法优势的实验证据。