Negotiation and Cooperation in Multi-Agent Environments

研究的目的是确定策略和学习技术的自动化代理，将能够成功地参与拍卖和谈判。拍卖和协商可用于解决各种多代理领域中的冲突。我们的主要应用领域将是电子商务。我们还将继续开发分布式信息服务器中文档分配的模拟。这些代理人没有足够的信息作为战略决策的基础。因此，我们建议为参与拍卖的代理人确定学习方法。我们将使用众所周知的学习技术，并将重点放在以下问题上：基于学习到的信息，智能体的期望有多准确？学习过程如何影响谈判和拍卖结果？最后，将进行人机实验，重点关注由自动代理收集和评估的信息将在多大程度上被拍卖中的人类参与者访问和利用。本研究的重要意义在于它有助于开发能够在不确定性和不完全信息的复杂环境中有效达成互利协议的自主代理。我们的研究结果将特别适用于电子商务和数字图书馆等新兴领域。

The research objective is to identify strategies and learning techniques for automated agents that will be able to participate successfully in auctions and negotiations. Auctions and negotiations can be used to resolve conflicts in a wide variety of multi-agent domains. Our main application domain will be electronic commerce. We will also continue to develop our simulation of document allocation in distributed information servers. These agents have insufficient information upon which to base strategic decisions. Thus, we proposeto identify learning methods for agents participating in auctions. We will use well known learning techniques and will focus on the questions: how accurate are the agent's expectations based on the learned information? and how does the learning process influence the negotiation and the auction outcome? Finally, human-computer experiments will be conducted focusing on the extent to which the information collected and evaluated by automated agents will be accessed and utilized by human participants in auctions. The significance of this research lies in its contribution to the development of autonomous agents capable of reaching mutually beneficial agreements efficiently in complex environments characterized by uncertainty and incomplete information. Our results will be particularly applicable to the emerging areas of electronic commerce and digital libraries.