摘要
为了克服单个agent知识的局限性,提高系统决策的可靠性,提出了一种基于D-S理论的多agent合作决策机制,并对多agent合作决策进行了定义和形式化描述.多agent合作决策划分为学习和决策两个阶段,学习阶段反馈信息的引入,使各agent根据正确的训练案例和修正公式实现了冲突消解,降低了合成计算的复杂度,改进了Dragoni等人的工作,较投票机制和加权多数算法具有更高的可靠性,实验结果验证了这一结论.
In order to overcome single agent's knowledge incompleteness and improve the reliability of decision-making results, a multi agent cooperative decision-making mechanism was proposed based on Dempster-Shafer theory. Formal description and definitions of the mechanism was given. The multi agent cooperative decision-making was divided into two stages: learning and decision-making. The correct feedback was introduced into the multi-agent's learning procedure. According to the training cases and revision formulas, the conflicts among agents were resolved and the complexity of combinational computation was decreased, thus improving the work of Dragoni et al. This multi-agent cooperative decision-making mechanism is more reliable than the voting mechanism and the weighted majority algorithm. The experiment results show its effectiveness.
基金
国家"863"计划(2003AA001021)
安徽省自然科学基金(00043302)资助