Abstract
Multi-Agent Systems (MASs) are increasingly accepted for modeling virtual complex distributed systems, such as virtual societies and smart grids, due to agents’ autonomy, flexibility and pro-activity. As autonomous, goal-driven agents can mislead others intentionally or accidentally by inaccurately reporting their competencies and abilities, the use of trust modeling deemed essential for successful interactions in MASs. The concept of trust is complex, multidimensional and includes more than just evaluating how honest interaction partners are. This study describes an explicit, multi-criteria, trust establishment model based on fuzzy logic to guide trustees in MASs to improve their level of trust as perceived by the trustor by tuning up their behaviors to attract more interactions with potential partners. When trustors are willing to provide feedback for interactions in the form of a single satisfaction value per multi-criterion interaction, the model attempts to predict the necessary improvement per criterion. We evaluated the performance of the proposed model using simulation. The results indicate that the model can help trustees achieve higher trust levels and get better chances to be selected as partners for interactions in MASs when trustors select trustees based on trust.
Highlights
Multi-Agent Systems (MASs) are increasingly accepted for modeling virtual complex systems such as e-commerce (Ramchurn et al, 2004) and smart grids (Moradi et al, 2016), where agents can come from various backgrounds with diverse capabilities, agents can join or leave the system and individual agents independently make decisions (Yu et al, 2013b)
Demand Level Effects average trust and utility gain under different demand levels are presented in Fig. 3 and 4, showing that MCFTE empowered trustees have a high average trust compared to Multi-Criteria Trust Establishment (MCTE) and higher average trust to Integrated Trust Establishment (ITE) and Reputational Incentive (RI) (Fig. 3, while providing a utility comparable to MCTE, but higher than the other two models (Fig. 4a and 4b) for normal and demanding trustors and considerably lower than the utility gain provided by MCTE
While the proposed model attempts to start making a profit as the level of satisfaction is high enough, providing various utility levels according to importance level helps trustees achieve a relatively high level of trust at a reasonable cost in terms of provided utility gain
Summary
Multi-Agent Systems (MASs) are increasingly accepted for modeling virtual complex systems such as e-commerce (Ramchurn et al, 2004) and smart grids (Moradi et al, 2016), where agents can come from various backgrounds with diverse capabilities, agents can join or leave the system and individual agents independently make decisions (Yu et al, 2013b). As autonomous agents in MASs may have their own costume set of beliefs and may make inaccurate statements regarding their abilities and competencies, there is a need for using effective trust assessment models to help trustors maximize interactions’ benefits (Burnett, 2011). In the domain of ad-hoc networks, the term refers to the trust evaluation of trustees from trustors' perspective, such as the work presented in (Saini and Gautam, 2011). Fuzzy Logic provides a non linear mapping of input data vector and scalar output (Mendel, 1995) where human knowledge can be presented as a set of basic rules in linguistic terms that maps the input into the corresponding output (Ross, 2010). A typical FLS contains an inference engine, a set of fuzzy logic rules, a fuzzifier and defuzzifier (Mendel, 1995). The concept of Membership Function (MF) determines the level to which a fuzzy variable is a member of a set where zero represents one extreme one represents the other extreme (Griffiths et al, 2006)
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