Fuzzy trust evaluation and credibility development in multi-agent systems

E-commerce markets can increase their efficiency through the usage of intelligent agents which negotiate and execute contracts on behalf of their owners. The measurement and computation of trust to secure interactions between autonomous agents is crucial for the success of automated e-commerce markets. Building a knowledge sharing network among peer agents helps to overcome trust-related boundaries in an environment where least human intervention is desired. Nevertheless, a risk management model which allows individual customisation to meet the different security needs of agent-owners is vital. The calculation and measurement of trust in unsupervised virtual communities like multi-agent environments involves complex aspects such as credibility rating for opinions delivered by peer agents, or the assessment of past experiences with the peer node one wishes to interact with. The deployment of suitable algorithms and models imitating human reasoning can help to solve these problems. This paper proposes not only a customisable trust evaluation model based on fuzzy logic but also demonstrates the integration of post-interaction processes like business interaction reviews and credibility adjustment. Fuzzy logic provides a natural framework to deal with uncertainty and the tolerance of imprecise data inputs to fuzzy-based systems makes fuzzy reasoning especially attractive for the subjective tasks of trust evaluation, business-interaction review and credibility adjustment.

[1]  Andy Oram,et al.  Peer-to-Peer: Harnessing the Power of Disruptive Technologies , 2001 .

[2]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[3]  Rüdiger Zarnekow,et al.  Intelligent software agents - foundations and applications , 1998 .

[4]  Shanshan Song,et al.  Trusted P2P transactions with fuzzy reputation aggregation , 2005, IEEE Internet Computing.

[5]  Michael Luck,et al.  Multi-Agent Systems and Applications , 2001, Lecture Notes in Computer Science.

[6]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[7]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[8]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[9]  Rino Falcone,et al.  Trust in information sources as a source for trust: a fuzzy approach , 2003, AAMAS '03.

[10]  Josep Lluís de la Rosa i Esteva,et al.  A fuzzy system based approach to social modeling in multi-agent systems , 2002, AAMAS '02.

[11]  Paul Resnick,et al.  Trust among strangers in internet transactions: Empirical analysis of eBay' s reputation system , 2002, The Economics of the Internet and E-commerce.

[12]  Piero P. Bonissone,et al.  A fuzzy sets based linguistic approach: Theory and applications , 1980, WSC '80.

[13]  Etienne E. Kerre,et al.  Defuzzification: criteria and classification , 1999, Fuzzy Sets Syst..

[14]  Lik Mui,et al.  Computational models of trust and reputation: agents, evolutionary games, and social networks , 2002 .

[15]  Detlef D. Nauck,et al.  Towards the automation of intelligent data analysis , 2006, Appl. Soft Comput..

[16]  Tharam S. Dillon,et al.  Building a Fuzzy Trust Network in Unsupervised Multi-agent Environments , 2005, OTM Workshops.

[17]  E. Michael Maximilien,et al.  Conceptual model of web service reputation , 2002, SGMD.

[18]  Ernesto Damiani,et al.  Choosing reputable servents in a P2P network , 2002, WWW.

[19]  Tharam S. Dillon,et al.  Managing the dynamic nature of trust , 2004 .

[20]  Jordi Sabater-Mir,et al.  Reputation and social network analysis in multi-agent systems , 2002, AAMAS '02.

[21]  Peter Smith,et al.  An automated negotiation mechanism based on co-evolution and game theory , 2002, SAC '02.

[22]  Martin J. Kollingbaum,et al.  Supervised interaction: creating a web of trust for contracting agents in electronic environments , 2002, AAMAS '02.

[23]  Robert Steele.  A Web Services-based system for ad-hoc mobile application integration , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[24]  E. Chang,et al.  Trust and Reputation for Service-Oriented Environments: Technologies For Building Business Intelligence And Consumer Confidence , 2006 .

[25]  Tharam S. Dillon,et al.  Applying a Fuzzy Trust Model to E-Commerce Systems , 2005, Australian Conference on Artificial Intelligence.

[26]  Ernesto Damiani,et al.  A reputation-based approach for choosing reliable resources in peer-to-peer networks , 2002, CCS '02.

[27]  Julita Vassileva,et al.  Bayesian network-based trust model , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[28]  Elpiniki I. Papageorgiou,et al.  A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps , 2005, Appl. Soft Comput..

[29]  Iyad Rahwan,et al.  Intelligent Agents for Automated One-to-Many E-Commerce Negotiation , 2002, ACSC.

[30]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .