基于Agent的情感劝说的信任识别模型研究
|
摘要
由于Agent系统的分布式特征,使得传统的安全手段已经不适用于新的应用需求,而解决该问题最重要的思路是信任管理,其基础之一就是信任识别。本文在Agent情感劝说的背景下,利用多维度评价信息和Agent关系网络,运用Dempter-Shafer证据理论,提出基于Agent的情感劝说的信任识别模型,引入动态权重因子,将直接交互信息和推荐信息进行组合,计算出各合作伙伴的综合信任度值,从中寻找出适合的合作伙伴,为Agent的决策提供大力的支持。
With the development of Internet technology and economy, transactions between organizations have become more and more frequent. As a necessary business activity, negotiation has become more and more important. Information, as an important resource, plays a vital role in the success of the negotiation.The negotiation mode based on Agent has greatly improved the efficiency of negotiation because of its integration into advanced information technology.However, because of the openness and uncertainty of the Agent system, the Agent system has also encountered many problems at the same time, and trust is one of the most important problems. In multi-Agent systems, Agent often represents different organizations or users, and in order to succeed, these Agent need to cooperate, coordinate and negotiate with other Agent to achieve their goals. This requires that the multi-Agent system can measure the trust degree of the interactive side, and the trust value of Agent is calculated by the trust model. The trust recognition model is established in this paper, and the appropriate partner is selected by this model.Under the background of Agent's emotional persuasion, using the multi dimension evaluation information and the Agent relation network and using the Dempter-Shafer evidence theory, the trust recognition model of emotional persuasion based on Agent is put forward, and the dynamic weighting factor is introduced to combine the direct interaction information and the recommendation information to calculate the comprehensive trust value of the partners. From that, we can find out suitable partners to provide strong support for Agent's decision making. The validity of the model is proved by an example.With the direct interaction history between Agent and the recommendation information of other Agents on the target Agent in the Agent relationship network, direct trust and indirect trust are combined with dynamic weighting factors. In computing trust, trust is considered as a combination concept, and the persuasion and emotional factors of target Agent in the interaction of the past are introduced. In comparison with human studies, the improvements and conclusions drawn from this study are summarized as follows:(1) Trust is a combinatorial concept and uses multi-dimensional evaluation information. This article is based on the ontology of trust, that is, trust is composed of many factors. The main factors that affect the trust of both parties are the quality of supply, the degree of supply completion in the prescribed time,the persuasion in the transaction and the pleasure of the cooperation between the two parties in the transaction, and the four dimensions of the transaction are fully utilized. The evaluation information, which completes the trustworthiness measurement of the service providers, improves its applicability and flexibility.(2) Direct trust and indirect trust are combined by dynamic weight factors, which is more close to reality. Direct trust and indirect trust are considered in the collection of trust information, and the dynamic weighting factor D is introduced in the combination of the two, that is, the weight is different with the number of direct interaction between the two parties. For example, when the buyer and target Agent have sufficient interaction experience, they will tend to give direct trust a larger weight. The combination of dynamic weight factors can make the results closer to reality.(3) Introducing emotion and persuasion influence factors to make the model more intelligent and adaptable to dynamic and complex environment. In real life, persuasion must be used in the process of negotiation. The strength of persuasion is also a strong degree of persuasion. The pleasure of the two parties also shows the satisfaction of the buyer with the results of the cooperation. The two factors are introduced into the trust model to make the model consider the emotional factors in the calculation. The calculation results are more practical.(4) Using Agent relational network to find recommender, to a large extent, avoid malicious recommendation. In this paper, the Agent relationship network is used to find the referer, because the information mobility of the Agent network is large, the information receiver is the potential collaborator of the two parties,and the breach of the cooperative party or the behavior that does not conform to the standard will affect the view of the transaction party in the next cooperation.Therefore, in the Agent relations network the default cost of Agent is very large, to a certain extent, and it can avoid malicious recommendation or breach of contract in cooperation.(5) It is a simple and effective way to get the trust evaluation by first hand material after grading each attribute of every transaction. After each transaction,key attributes, such as the quality of the service or goods, the degree of delivery completion within the specified time, the persuasion in the process of transaction, the degree of pleasure in the transaction process and the outcome, and the hierarchical information accumulated over a number of services to obtain the trust evaluation of the target Agent, can be used to improve partner selection efficiency.
With the development of Internet technology and economy, transactions between organizations have become more and more frequent. As a necessary business activity, negotiation has become more and more important. Information, as an important resource, plays a vital role in the success of the negotiation.The negotiation mode based on Agent has greatly improved the efficiency of negotiation because of its integration into advanced information technology.However, because of the openness and uncertainty of the Agent system, the Agent system has also encountered many problems at the same time, and trust is one of the most important problems. In multi-Agent systems, Agent often represents different organizations or users, and in order to succeed, these Agent need to cooperate, coordinate and negotiate with other Agent to achieve their goals. This requires that the multi-Agent system can measure the trust degree of the interactive side, and the trust value of Agent is calculated by the trust model. The trust recognition model is established in this paper, and the appropriate partner is selected by this model.Under the background of Agent's emotional persuasion, using the multi dimension evaluation information and the Agent relation network and using the Dempter-Shafer evidence theory, the trust recognition model of emotional persuasion based on Agent is put forward, and the dynamic weighting factor is introduced to combine the direct interaction information and the recommendation information to calculate the comprehensive trust value of the partners. From that, we can find out suitable partners to provide strong support for Agent's decision making. The validity of the model is proved by an example.With the direct interaction history between Agent and the recommendation information of other Agents on the target Agent in the Agent relationship network, direct trust and indirect trust are combined with dynamic weighting factors. In computing trust, trust is considered as a combination concept, and the persuasion and emotional factors of target Agent in the interaction of the past are introduced. In comparison with human studies, the improvements and conclusions drawn from this study are summarized as follows:(1) Trust is a combinatorial concept and uses multi-dimensional evaluation information. This article is based on the ontology of trust, that is, trust is composed of many factors. The main factors that affect the trust of both parties are the quality of supply, the degree of supply completion in the prescribed time,the persuasion in the transaction and the pleasure of the cooperation between the two parties in the transaction, and the four dimensions of the transaction are fully utilized. The evaluation information, which completes the trustworthiness measurement of the service providers, improves its applicability and flexibility.(2) Direct trust and indirect trust are combined by dynamic weight factors, which is more close to reality. Direct trust and indirect trust are considered in the collection of trust information, and the dynamic weighting factor D is introduced in the combination of the two, that is, the weight is different with the number of direct interaction between the two parties. For example, when the buyer and target Agent have sufficient interaction experience, they will tend to give direct trust a larger weight. The combination of dynamic weight factors can make the results closer to reality.(3) Introducing emotion and persuasion influence factors to make the model more intelligent and adaptable to dynamic and complex environment. In real life, persuasion must be used in the process of negotiation. The strength of persuasion is also a strong degree of persuasion. The pleasure of the two parties also shows the satisfaction of the buyer with the results of the cooperation. The two factors are introduced into the trust model to make the model consider the emotional factors in the calculation. The calculation results are more practical.(4) Using Agent relational network to find recommender, to a large extent, avoid malicious recommendation. In this paper, the Agent relationship network is used to find the referer, because the information mobility of the Agent network is large, the information receiver is the potential collaborator of the two parties,and the breach of the cooperative party or the behavior that does not conform to the standard will affect the view of the transaction party in the next cooperation.Therefore, in the Agent relations network the default cost of Agent is very large, to a certain extent, and it can avoid malicious recommendation or breach of contract in cooperation.(5) It is a simple and effective way to get the trust evaluation by first hand material after grading each attribute of every transaction. After each transaction,key attributes, such as the quality of the service or goods, the degree of delivery completion within the specified time, the persuasion in the process of transaction, the degree of pleasure in the transaction process and the outcome, and the hierarchical information accumulated over a number of services to obtain the trust evaluation of the target Agent, can be used to improve partner selection efficiency.
引文
[1]张明清,范涛,唐俊,等.基于Agent分布式系统信任模型仿真[J].计算机工程与设计,2014,35(9):3202-3206.
[2]柯小路.证据理论中信任函数的合成方法研究与应用[D].中国科学技术大学,2016.
[3]Basheer G S,Ahmad M S,Tang A Y C,et al.Certainty,trust and evidence:Towards an integrative model of confidence in multi-agent systems[J].Computers in Human Behavior,2015,45:307-315.
[4]Yu H,Shen Z,Leung C,et al.A Survey of Multi-Agent Trust Management Systems[J].IEEE Access,2013,1(1):35-50.
[5]伍京华,蒋国瑞,孙华梅,等.基于Agent的辩论谈判过程建模与系统实现[J].管理工程学报,2008,22(3):69-73.
[6]赵书良,蒋国瑞,黄梯云.基于信用和关系网的Multi-agent System信任体系[J].计算机工程,2006,32(8):198-200.
[7]赵书良,蒋国瑞,黄梯云.一种Multi-agent System的信任模型[J].管理科学学报,2006,9(5):36-43.
[8]张高旭.基于多属性评价的信任模型研究[D].天津大学,2016.
[9]杨兴燚.基于多Agent信任机制的电子商务谈判系统研究[D].厦门大学,2014.
[10]Ramchurn S,Jennings N,CARLESSIERRA,et al.Devising a trust model for multi-agent interactions using confidence and reputation[J].Applied Artificial Intelligence,2004,18(9-10):833-852.
[11]Basheer G S,Ahmad M S,Tang A Y C,et al.Certainty,trust and evidence:Towards an integrative model of confidence in multi-agent systems[J].Computers in Human Behavior,2015,45:307-315.
[12]Leila Amgoud,Henri Prade.Handling threats,rewards and explanatory arguments in a unified setting.In International Journal Of Intelligent Systems,Wiley periodical Inc.2005,20(12):1195-1218.
[13]杨佩,高阳,陈兆乾.一种劝说式多Agent多议题协商方法[J].计算机研究与发展,2006,43(7):1149-1154.
[14]董学杰.基于情感模型的辩论谈判系统研究[D].北京工业大学,2013.
[15]Santos R,Marreiros G,Ramos C,et al.Personality,Emotion,and Mood in Agent-Based Group Decision Making[J].IEEE Intelligent Systems,2011,26(6):58-66.
[16]Wang S,Wei S.Research on multi-agent system based trust model of partner selection of virtual enterprise[C]//International Conference on Networked Digital Technologies.IEEE,2009:512-514.
[17]曹聪梅,甘仞初,吴菊华,等.基于多Agent的合作伙伴选择协商模型[J].计算机工程与设计,2006,27(4):629-632.
[18]栾玉琪.基于Agent模型的社会推荐系统分析研究[D].华中师范大学,2016.
[19]赵书良.多智能体合作理论与方法及其在商务智能中的应用[D].北京工业大学,2006.
[20]Hang C W,Zhang Z,Singh M P.Shin:Generalized Trust Propagation with Limited Evidence[J].Computer,2013,46(3):78-85.
[21]孙怀江.开放多Agent系统信任管理中的信任获取方法研究[J].计算机工程与应用,2004,40(29):135-138.
[22]隋新,蔡国永,史磊,等.多Agent合作中的信任模型研究[J].计算机工程,2010,36(24):116-118.
[23]王玲玲,童向荣.基于多目标优化的多Agent信任联盟模型[J].济南大学学报(自然科学版),2015,29(5):350-354.
[24]彭泽洲.基于社会网络与声誉信任机制的移动多Agent系统信任模型[J].计算机应用与软件,2012,29(8):190-192.
[25]黄巧华,黄穗.基于多Agent的医疗信任模型的模拟[J].计算机应用与软件,2011,28(5):129-130.
[26]Xu X,Bessis N,Cao J.An Autonomic Agent Trust Model for IoTsystems[J].Procedia Computer Science,2013,21:107-113.
[27]Shi Z T,Zeng J C.Agent-Based Enterprise Relationship Network Evolving Model[J].Journal of Taiyuan University of Science&Technology,2014.
[28]贺利坚,黄厚宽.MAS中信任和信誉系统的研究进展[J].计算机科学,2011,38(4):1-8.
[29]陈广福.多Agent系统环境下动态信任模型研究[D].桂林电子科技大学,2011.
[30]Bagheri E,Zafarani R,Barouni-Ebrahimi M.Can reputation migrate?On the propagation of reputation in multi-context communities[J].Knowledge-Based Systems,2009,22(6):410-420.
[31]王平.多Agent系统中的信任模型研究[D].西南师范大学西南大学,2005.
[2]柯小路.证据理论中信任函数的合成方法研究与应用[D].中国科学技术大学,2016.
[3]Basheer G S,Ahmad M S,Tang A Y C,et al.Certainty,trust and evidence:Towards an integrative model of confidence in multi-agent systems[J].Computers in Human Behavior,2015,45:307-315.
[4]Yu H,Shen Z,Leung C,et al.A Survey of Multi-Agent Trust Management Systems[J].IEEE Access,2013,1(1):35-50.
[5]伍京华,蒋国瑞,孙华梅,等.基于Agent的辩论谈判过程建模与系统实现[J].管理工程学报,2008,22(3):69-73.
[6]赵书良,蒋国瑞,黄梯云.基于信用和关系网的Multi-agent System信任体系[J].计算机工程,2006,32(8):198-200.
[7]赵书良,蒋国瑞,黄梯云.一种Multi-agent System的信任模型[J].管理科学学报,2006,9(5):36-43.
[8]张高旭.基于多属性评价的信任模型研究[D].天津大学,2016.
[9]杨兴燚.基于多Agent信任机制的电子商务谈判系统研究[D].厦门大学,2014.
[10]Ramchurn S,Jennings N,CARLESSIERRA,et al.Devising a trust model for multi-agent interactions using confidence and reputation[J].Applied Artificial Intelligence,2004,18(9-10):833-852.
[11]Basheer G S,Ahmad M S,Tang A Y C,et al.Certainty,trust and evidence:Towards an integrative model of confidence in multi-agent systems[J].Computers in Human Behavior,2015,45:307-315.
[12]Leila Amgoud,Henri Prade.Handling threats,rewards and explanatory arguments in a unified setting.In International Journal Of Intelligent Systems,Wiley periodical Inc.2005,20(12):1195-1218.
[13]杨佩,高阳,陈兆乾.一种劝说式多Agent多议题协商方法[J].计算机研究与发展,2006,43(7):1149-1154.
[14]董学杰.基于情感模型的辩论谈判系统研究[D].北京工业大学,2013.
[15]Santos R,Marreiros G,Ramos C,et al.Personality,Emotion,and Mood in Agent-Based Group Decision Making[J].IEEE Intelligent Systems,2011,26(6):58-66.
[16]Wang S,Wei S.Research on multi-agent system based trust model of partner selection of virtual enterprise[C]//International Conference on Networked Digital Technologies.IEEE,2009:512-514.
[17]曹聪梅,甘仞初,吴菊华,等.基于多Agent的合作伙伴选择协商模型[J].计算机工程与设计,2006,27(4):629-632.
[18]栾玉琪.基于Agent模型的社会推荐系统分析研究[D].华中师范大学,2016.
[19]赵书良.多智能体合作理论与方法及其在商务智能中的应用[D].北京工业大学,2006.
[20]Hang C W,Zhang Z,Singh M P.Shin:Generalized Trust Propagation with Limited Evidence[J].Computer,2013,46(3):78-85.
[21]孙怀江.开放多Agent系统信任管理中的信任获取方法研究[J].计算机工程与应用,2004,40(29):135-138.
[22]隋新,蔡国永,史磊,等.多Agent合作中的信任模型研究[J].计算机工程,2010,36(24):116-118.
[23]王玲玲,童向荣.基于多目标优化的多Agent信任联盟模型[J].济南大学学报(自然科学版),2015,29(5):350-354.
[24]彭泽洲.基于社会网络与声誉信任机制的移动多Agent系统信任模型[J].计算机应用与软件,2012,29(8):190-192.
[25]黄巧华,黄穗.基于多Agent的医疗信任模型的模拟[J].计算机应用与软件,2011,28(5):129-130.
[26]Xu X,Bessis N,Cao J.An Autonomic Agent Trust Model for IoTsystems[J].Procedia Computer Science,2013,21:107-113.
[27]Shi Z T,Zeng J C.Agent-Based Enterprise Relationship Network Evolving Model[J].Journal of Taiyuan University of Science&Technology,2014.
[28]贺利坚,黄厚宽.MAS中信任和信誉系统的研究进展[J].计算机科学,2011,38(4):1-8.
[29]陈广福.多Agent系统环境下动态信任模型研究[D].桂林电子科技大学,2011.
[30]Bagheri E,Zafarani R,Barouni-Ebrahimi M.Can reputation migrate?On the propagation of reputation in multi-context communities[J].Knowledge-Based Systems,2009,22(6):410-420.
[31]王平.多Agent系统中的信任模型研究[D].西南师范大学西南大学,2005.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |