Learning in Negotiation: A Sequential Decision Making Model and Applications

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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.