Computer Science ›› 2019, Vol. 46 ›› Issue (12): 220-230.doi: 10.11896/jsjkx.190800129

• Artificial Intelligence • Previous Articles     Next Articles

A Survey of Automated Negotiation and Its Fuzzy Set Based Models

LUO Xu-dong1, HUANG Qiao-juan1, ZHAN Jie-yu2   

  1. (College of Computer Science and Information Engineering,Guangxi Normal University,Guilin,Guangxi 541000,China)1;
    (School of Computer Science,South China Normal University,Guangzhou 510631,China)2
  • Received:2019-05-27 Online:2019-12-15 Published:2019-12-17

Abstract: Negotiation is a process in which two or more interested parties communicate with each other for an agreement,automated negotiation isto use artificial intelligence system to automate this process.This paper first discussed the significance of studying automated negotiation.At the national level,the research and development of such systems is consistent with the national AI development strategy of China.It can not only help people live a better life in the e-commerce era,but also reduce corruption by using computer systems instead of manual negotiation.Secondly,the deve-lopment of negotiation proceeds in game theory,management science and computer science were outlined.The Nobel laureate in economics,Nash,started the study of negotiation in the field of game theory.However,the biggest problem in such study is the assumption that all information of negotiating parties involved is essentially public,which is unrealistic.Therefore,researchers in management science focus on how to conduct manual negotiation when all information of all parties is not disclosed.In general,manual negotiation is inefficient and difficult,and often fails to achieve optimal results.As a result,computer scientists are trying to replace manual negotiate with madnines.At the beginning,they focused on the computer-to-computer automated negotiation.Now their focus is turning to computer-to-human automated negotiation.Finally,methods of using fuzzy logic and fuzzy constraints to develop intelligent agents for automated negotiation were summarised.This paper focused on the application of these two fuzzy methods in the automatic negotiation system and the practical application of fuzzy automatic negotiation system.

Key words: Automated negotiation, Artificial intelligence, Electronic commerce, Intelligent agent, Fuzzy logic, Fuzzy constraint, Machine learning

CLC Number: 

  • TP18
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[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[9] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .
[10] YANG Yu-qi, ZHANG Guo-an and JIN Xi-long. Dual-cluster-head Routing Protocol Based on Vehicle Density in VANETs[J]. Computer Science, 2018, 45(4): 126 -130 .