摘要
在Agent双边协商过程中往往包含对多个议题的协商.针对以往的基于议程、相似度、案例等协商方法中大部分都忽略了议题取值之间可能存在的依赖关系,提出一种面向议题关联的双边多议题协商模型.首先模型结合了多议题顺序协商思想和局部接受协商策略;其次引入离线学习机制,对协商成功的历史记录进行分区离线学习,利用离线学习机制产生的议题关联规则与预测神经网络实现对关联议题可能接受取值的预测;最后模型提出一种基于关联预测值的分段时间协商策略.实验结果表明,该模型在一定程度上提高了协商的总体效用值和效率.
In bilateral negotiation procedures, there often exist a number of issues. Models based on agenda, similarity or cases, ignore in most cases the interdependence of values of each issue. This paper proposes an interdependence-oriented bilateral multi-issue negotiation model. Firstly, the model adopts the thoughts of sequential procedure and local acceptance strategy for multi-issues negotiation. Secondly, it introduces off-line learning mechanism to partition the successful historical records of learning and uses these association rules and neural networks, which are generated from off-line learning to predict the acceptable values of interdependent issues. Lastly, this model presents a segmentation time strategy, which is based on the interdependent predicted value. The experiment results have shown that this model can improve the overall utility and efficiency to some extent.
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第12期66-71,共6页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(60773208)
湖南省自然科学基金资助项目(11JJ3065)
高等学校博士学科点专项科研基金资助项目(20070532075)
湖南省普通高校青年骨干教师培养对象项目
关键词
多AGENT系统
多议题协商
关联规则
分段时间策略
multi Agent systems
multi-issue negotiation
association rules
segmentation time strategy
