Learning in Negotiation: A Sequential Decision Making Model and Applications

这是一个三年的标准奖项。本研究旨在建立一个独立于领域的协商计算模型，该模型能够处理多议题协商和不完全信息下的决策等复杂问题。本研究基于谈判的顺序决策观，为谈判的多阶段性提供了一种自然的表征。诸如与更新对部分已知世界的信念相关的学习等问题将得到解决。将原来的基于顺序决策的谈判模型进行扩展，以明确地模拟谈判的战略部分。通过应用动态规划策略，可以使所得到的形式在计算上易于处理。在该模型下，许多关键问题，如agent之间的信息不对称、谈判的动态过程、环境的变化等，都可以通过实验进行分析和探讨。此外，该研究将为新兴的多智能体学习领域做出贡献。将开发计算效率高的多智能体学习算法，并探讨在模型中引入学习的影响。为了评估这一研究，将开发一个多智能体模拟试验台并利用它进行实证研究。这些研究将针对重要的理论和实践问题，例如在供应合同等领域的实际问题场景中，不同的谈判策略和学习算法的有效性。

This is a three year standard award. The research aims to develop a domain-independent computational model of negotiation capable of addressing several complex issues, such as multi-issue negotiation and decision-making under incomplete information. The research is based on a sequential decision-making view of negotiation that provides a natural representation of the multi-stage nature of negotiation. Issues such as learning associated with updating beliefs about a partially-known world will be addressed. The original sequential-decision-making-based negotiation model will be extended to explicitly model strategic parts of negotiation. The resulting formalism can be made computationally tractable by applying dynamic programming strategies. Under this model, many key issues, such as asymmetric information among agents, dynamic processes of negotiation, changing environments, etc. can be analyzed and explored experimentally. In addition, the research will contribute to the emerging field of multi-agent learning. Computationally efficient multi-agent learning algorithms will be developed and the impact of introducing learning in the model will be explored. To evaluate this research, a multi-agent simulation testbed will be developed and utilized to conduct empirical studies. These studies will be directed toward significant theoretical and practical questions, such as the effectiveness of different negotiation strategies and learning algorithms in realistic problem scenarios from domains such as supply contracting.