Beliefs learning in fuzzy constraint-directed agent negotiation

This paper presents a belief learning model for fuzzy constraint-directed agent negotiation. The main features of the proposed model include: 1) fuzzy probability constraints for increasing the efficiency on the convergence of behavior patterns, and eliminating the noisy hypotheses or beliefs, 2) fuzzy instance matching method for reusing the prior opponent knowledge to speed up the problem-solving, and inferring the proximate regularities to acquire a desirable result on forecasting opponent behavior, and 3) adaptive interaction for making a dynamic concession to fulfill a desirable objective. Experimental results suggest that the proposed framework can improve both negotiation qualities.