基于RBF神经网络和强化学习算法的供应链产销协同计划冲突消解研究  

Research on conflict resolution of supply chain production-marketing collaborative planning based on RBF and Q-reinforcement

作　　者：武玉英[1] 李豪[1] 蒋国瑞[1] 

机构地区：[1]北京工业大学经济管理学院,北京100022

出　　处：《计算机应用研究》2015年第5期1335-1338,1344,共5页Application Research of Computers

基　　金：国家自然科学基金面上资助项目(71371018);北京市社科规划项目(13JDJGB037)

摘　　要：为提高传统协商自学习能力,利用多agent智能技术,建立基于黑板模型的协商框架,构建五元组协商模型,采取Q-强化学习算法,给出一种协商策略;使用RBF神经网络进一步优化协商策略,预测对手信息并调整让步幅度。通过算例验证该方法的可行性和有效性,通过与未改进的Q-强化学习算法对比,该方法可增强协商agent的自学习能力,缩短协商时间,提高冲突消解效率。In order to improve the self-learning ability of traditional negotiation,this paper integrated multi-agent intelligent technology,designed the negotiation framework based on the blackboard model,constructed the five-elements negotiation model,adopted the negotiation strategy based on Q-reinforcement learning,proposed a negotiation strategy; then it optimized the negotiation strategy by the RBF neural network,predicted the information of opponent for adjusting the concession extent.At last,it verifies the feasibility and validity of the algorithm through a sample application. When comparing to the un-optimized Q-reinforcement learning,it can enhance the learning ability of the negotiation agents,reduce the negotiation time,and improve the efficiency of resolving conflicts.

关 键 词：多AGENT 自学习 RBF神经网络 Q-强化学习 冲突消解 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] TP183[自动化与计算机技术—计算机科学与技术]