Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents
Agent negotiation is a form of decision making where two or more agents jointly search for a mutually agreed solution to a certain problem. In multi-issue negotiation, with information available about the agents' preferences, a negotiation may result in a mutually beneficial agreement. In a competitive negotiation environment, however, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a mutually beneficial agreement. In order to solve this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent's preference in bilateral multi-issue negotiation. The proposed approach employs Bayesian theory to analyse the opponent's historical offers and to approximately predict the opponent's preference over negotiation issues. A counter-offer proposition algorithm is also integrated into the prediction approach to help agents to propose mutually beneficial offers based on the prediction results. Experimental results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency.