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 propose to 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.
