Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2016, Vol. 17 Issue (4): 296-308    DOI: 10.1631/FITEE.1500344
    
应用于普适群体决策的智能谈判模型
Jo?o Carneiro, Diogo Martinho, Goreti Marreiros, Paulo Novais
GECAD-Knowledge Engineering and Decision Support Group, Institute of Engineering, Polytechnic of Porto, Porto 4200-072, Portugal; ALGORITMI Centre, University of Minho, Guimar?es 4800-058, Portugal
Intelligent negotiation model for ubiquitous group decision scenarios
Jo?o Carneiro, Diogo Martinho, Goreti Marreiros, Paulo Novais
GECAD-Knowledge Engineering and Decision Support Group, Institute of Engineering, Polytechnic of Porto, Porto 4200-072, Portugal; ALGORITMI Centre, University of Minho, Guimar?es 4800-058, Portugal
 全文: PDF 
摘要： 目的：开发一种应用于普适情形的群体（对话）决策模型。
创新点：遵循社交网络逻辑，提出了一种贴近真实情景的智能群体决策的理论模型，更好地支持普通背景的群体决策过程。
方法：首先分析了现有的群体决策支持系统（GDSS）模型，并指出现有模型的缺陷：极少能够商业应用且难以反映决策过程的自然规律；而有较大发展空间的基于辩论的谈判模型的研究成果甚少。然后提出了一种更贴近真实情景的普适群体决策模型。该模型基于社交网络中的交流逻辑，重新定义了多标准问题的属性以及智能体推理与对话等，综合考虑了群体目标和群体中个人目标。所提出的群体谈判模型允许研究人员进一步基于自己的算法、论点以及智能体模型进行扩展。运用案例分析论证了模型的可行性。
结论：本文提出的理论模型能够支持普适的群体决策过程，保证面对面交流的质量。
关键词： 群体决策支持系统;  普适计算;  自动谈判;  社交网络    
Abstract: Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specifically designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms, and agents’ modeling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problem by considering and defining strategies to deal with important points such as the type of attributes in the multi-criterion problems, agents’ reasoning, and intelligent dialogues.
Key words: Group decision support systems    Ubiquitous computing    Automatic negotiation    Social networks    Multi-agent systems
收稿日期: 2015-10-18 出版日期: 2016-04-05
CLC:  TP181  
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Jo?o Carneiro
Diogo Martinho
Goreti Marreiros
Paulo Novais

引用本文:

Jo?o Carneiro, Diogo Martinho, Goreti Marreiros, Paulo Novais. Intelligent negotiation model for ubiquitous group decision scenarios. Front. Inform. Technol. Electron. Eng., 2016, 17(4): 296-308.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/FITEE.1500344    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2016/V17/I4/296

[1] Jian-ru Xue, Di Wang, Shao-yi Du, Di-xiao Cui, Yong Huang, Nan-ning Zheng. 无人车自主定位和障碍物感知的视觉主导多传感器融合方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 122-138.
[2] Tao-cheng Hu, Jin-hui Yu. 基于最大间隔的贝叶斯分类器[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(10): 973-981.
[3] Izabela Nielsen, Robert Wójcik, Grzegorz Bocewicz, Zbigniew Banaszak. 模糊操作时间约束下的多模过程优化：声明式建模方法[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(4): 338-347.
[4] Ya-tao Zhang, Cheng-yu Liu, Shou-shui Wei, Chang-zhi Wei, Fei-fei Liu. 基于非线性支持向量机和遗传算法的移动ECG质量评估[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(7): 564-573.
[5] Feng-fei Zhao, Zheng Qin, Zhuo Shao, Jun Fang, Bo-yan Ren. 用于在线值函数近似的贪婪特征替换方法[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(3): 223-231.
[6] Hong-xia Pang, Wen-de Dong, Zhi-hai Xu, Hua-jun Feng, Qi Li, Yue-ting Chen. Novel linear search for support vector machine parameter selection[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(11): 885-896.
[7] Zhi-yong Yan, Cong-fu Xu, Yun-he Pan. [J]. Frontiers of Information Technology & Electronic Engineering, 2011, 12(8): 647-657.
[8] Peng Chen, Yong-zai Lu. Extremal optimization for optimizing kernel function and its parameters in support vector regression[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(4): 297-306.
[9] Zhuo-jun Jin, Hui Qian, Shen-yi Chen, Miao-liang Zhu. Convergence analysis of an incremental approach to online inverse reinforcement learning[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(1): 17-24.
[10] Shen-yi Chen, Hui Qian, Jia Fan, Zhuo-jun Jin, Miao-liang Zhu. Modified reward function on abstract features in inverse reinforcement learning[J]. Front. Inform. Technol. Electron. Eng., 2010, 11(9): 718-723.