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An Intelligent Scheme for Concurrent Multi-Issue Negotiations
Automated negotiations are an active research field for many years. In negotiations, participants’ characteristics play a crucial role to the final result. The most important characteristics are the deadline and the strategy of the entities. The deadline defines the time for which each entity will participate in the negotiation while the strategy defines the proposed prices at every round. In this paper, we focus on the buyer side and study multi-issue concurrent negotiations between a buyer and a set of sellers. In this setting, the buyer adopts a number of threads. We propose the use of known optimization techniques for updating the buyer behavior as well as a methodology based on the known Particle Swarm Optimization (PSO) algorithm for threads coordination. The PSO algorithm is used to lead the buyer to the optimal solution (best deal) through threads team work. Hence, we are able to provide an efficient mechanism for decision making in the buyer’s side. In real situations, there is absolutely no knowledge on the characteristics of the involved entities. We combine the proposed methods adopting the Kernel Density Estimator (KDE) and Fuzzy Logic (FL) in order to handle incomplete knowledge on entities characteristics. When an agreement is true in the set of threads, KDE is responsible to provide to the rest of them the opportunity to calculate the probability of having a better agreement or not. The result of the KDE is fed to a FL controller in order to adapt the behavior of each thread. Our experiments depict the efficiency of the proposed techniques through numerical results derived for known evaluation parameters.
Multi-Issue Concurrent Negotiations, PSO, Optimization, Fuzzy Logic, KDE
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