A Survey of Opponent Modeling Techniques in Automated Negotiation

Negotiation is a process in which parties interact to settle a mutual concern to improve their status quo. Traditionally, negotiation is a necessary, but time-consuming and expensive activity. Therefore, in the last two decades, there has been a growing interest in the automation of negotiation. One of the key challenges for a successful negotiation is that usually only limited information is available about the opponent. Although sharing private information can result in mutual gains, negotiators often avoid this to prevent exploitation. This problem can be partially overcome by deriving information from the opponent’s actions. Exploiting this information to learn aspects of the opponent is called opponent modeling. Creating an accurate opponent model is a key factor in improving the quality of the outcome and can further increase the benefits of automated negotiation. Despite the advantages of opponent modeling and two decades of research, there is no recent study that provides an overview of the field. Therefore, in order to stimulate the development of efficient future opponent models, and to outline a research agenda, we provide an overview of existing opponent models in bilateral negotiation [2]. As our main contributions, we classify opponent models using a comprehensive taxonomy and provide recommendations on how to select the best model depending on the negotiation setting.