Negotiation is any process through which the players on their own try to reach an agreement. It is a task that has a broad spectrum of practical applications to a variety of social, economic, and politic phenomena. When it comes to complicated problems such as negotiations with a large number of issues, finding good agreements is however a tough challenge for human beings, especially in the case that they lack negotiation experience, opponent information, and the available negotiation time is limited. In order to overcome these limitations, there exists considerable interest in automating their negotiation process by means of software agents to assist humans in the decision-making process. Automated negotiation therefore provides people with a realistic alternative solution. This chapter first overviews forms, protocols, and three main approaches of automated negotiation, namely, heuristic, game theoretic, and argumentation approaches. Then, the focus is on the study of complex practical negotiation — multiissue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents’ preferences and strategies. Finally, two classes of state-of-the-art negotiation agents for complex negotiation are presented, namely, the agents based on regression techniques and the agents based on transfer learning to support its decision-making process during negotiation.
|Title of host publication||Interactions in Multiagent Systems|
|Publisher||World Scientific Publishing Company|
|Publication status||Published - 2018|