Research On Trust And Negotiation In Cooperative Solving Of Multiple Agents

Generally, there are iterative interactions among agents in large open environment. So each agent usually forms its own interaction history. Sequentially, intelligent agent would derive some useful information and knowledge from history data to be reused in the future for optimizing the performance of interactions.Trust and negotiation are two important problems in cooperative solving of multi-agent systems. Trust is the base of cooperative solving and negotiation is a basic method of cooperative solving. So it is necessary to research on trust and negotiation together. Computation of trust is an interesting direction in multi-agent systems, for good trust relationship would guarantee the success of the future interactions in large open environment. Furthermore, multi-issue negotiation with incomplete information is always an important and challenging problem in cooperative solving. Unfortunately, trust and negotiation have not been resolved ideally up to now. It is the motivation of this dissertation to obtain optimal methods of trust and negotiation from the history of interactions.There are some shortages in the researches of trust and negotiation of multi-agent systems.Previous work on trust is only based on the average probability of historical interactions and there is a lack of attention to dynamic variety of agent trust. So the ability of precise prediction of trust and abnormal behavior detection is not satisfied. Few studies have been done on agent coalition credit to this day and there is a need to investigate it in detail. Furthermore, there are lots of imprecise and lying information in large open environment, which leads to a low confidence of trust computation.Previous work on multi-issue negotiation usually uses indirect approaches to acquire the preferences of the opponent such as a variety of data mining methods. On the other hand, agents usually have some negotiation experiences and domain knowledge which may help them get better negotiation results. Furthermore, the choice of utility functions has not been paid more attention to. Previous papers mostly adopted linear utility functions which is not widely used in most circumstances.To this end, this dissertation introduces the followings.(1) We propose a computational model of agent dynamic interaction trust (CMAIT), where interaction history is divided by time. Sequentially, based on the first derivative of trust, we give the confidence of computational information and that of computational deviation of CMAIT. The mechanism of abnormal behavior's detection of CMAIT is also given. We conduct Experiments on E-commerce at taobao website. Experimental results demonstrate that the computational error of CMAIT is half of that of TRAVOS model and its computational complexity is also lower than TRAVOS model. It improves the work of Jennings on agent trust.(2) We present a long-term coalition credit model (LCCM). Sequentially, the relationship between coalition credit and coalition payoff is also given. Generalization of LCCM can be demonstrated through experiments applied in both cooperative and competitive environment. Experimental results show that LCCM is capable of coalition credit computation efficiently and can properly reflect the effect of various factors on coalition credit.(3) We propose an agent multi-issue negotiation model under incomplete information based on cases and game theory. The computational complexity of the proposed algorithm is polynomial order and it is commonly lower than that of Fatima, as long as the scale of cases base is limited to a bounded quantities. Experimental results indicate that the utility and the reaching time of our experiments have an advantage of that of human beings and the method of Lin. It improves the work of Fatima.(4) We expand linear utility function to a nonlinear one. Particularly, we propose an improved utility function based on sigmoid function in neural network, according to the principle of marginal utility decreasing. Sequentially, we present a negotiation model over multiple divisible resources with two phases, as well as its feasible algorithm. The computational complexity of this model is polynomial order. Experimental results show that the optimal efficiency of this model takes an advantage over the previous work.