带有目标偏好的最大成功联盟生成算法  

Algorithm for Maximal Successful Coalition Generation with Goals Preferences

作　　者：张国富[1,2] 杜晓东[1,2] 苏兆品[1,2] 蒋建国[1,2] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009 [2]合肥工业大学安全关键工业测控技术教育部工程研究中心,合肥230009

出　　处：《模式识别与人工智能》2017年第6期489-498,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61573125;61371155);安徽省自然科学基金项目(No.1608085MF131;1508085MF132;1508085QF129)资助~~

摘　　要：资源结盟博弈(CRGs)研究均假设每个agent可以响应所有目标,即使目标不在其感兴趣的子目标集内.针对此问题,文中提出带有目标偏好的CRGs模型,即每个agent只愿意把自己的有限资源贡献给自己的兴趣集中的目标.此外,设计基于二维二进制编码的最大成功联盟生成算法,并提出编码修正启发式算法解决多个目标竞争同一agent资源可能引起的的资源冲突.最后,通过与已有相关算法的对比实验验证文中算法的有效性.In the traditional research on coalitional resource games （CRGs）, it is assumed that an agent can respond to any goal, even if the agent is not interested in the goal at all. In this paper, a natural variation of CRGs with goals preferences is discussed. An agent only contributes its resources to the goals in its own goal （or interest） set. For this purpose, an improved CRGs model is fir@ proposed on the basis of goals preferences. Moreover, a two-dimensional binary encoding based algorithm for maximal successful coalition （MAXSC） generation is designed and a heuristic algorithm is developed to resolve the potential conflicts of agents scrambling for scarce resources. Finally, the proposed approach is compared with the previous algorithms for the MAXSC problem. The results demonstrate the effectiveness of the proposed aonroach.

关 键 词：资源结盟博弈(CRGs) 目标偏好 最大成功联盟 启发式算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]