A fuzzy inference automatic negotiation system with bayesian learning

Real-world negotiations are characterized by complex negotiation spaces, tough deadlines, bounded agent rationality, very limited information about the opponents, and volatile negotiator preferences. Classical negotiation models fail to address most of these issues. Practical negotiation agents with an effective and efficient fuzzy inference to deal with complex and incomplete negotiation spaces arising in real-world applications are proposed. The agent with the fuzzy inference determines the values of the new offer through the set of fuzzy rules. An evolutionary algorithm with Bayesian learning of its opponents´ preferences according to the history of the counter offers and genetic algorithms (GA) are used to optimize the parameters of the fuzzy rules. Simulation shows that responsive and adaptive negotiation agents work for real-world negotiations.