Conference Paper

On the Reputation of Agent-Based Web Services.

Conference: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010
Source: DBLP
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    ABSTRACT: In competitive electronic marketplaces where some selling agents may be dishonest and quality products offered by good sellers are limited, selecting the most profitable sellers as transaction partners is challenging, especially when buying agents lack personal experience with sellers. Reputation systems help buyers to select sellers by aggregating seller information reported by other buyers (called advisers). However, in such competitive marketplaces, buyers may also be concerned about the possibility of losing business opportunities with good sellers if they report truthful seller information. In this paper, we propose a trust-oriented mechanism built on a game theoretic basis for buyers to: (1) determine an optimal seller reporting strategy, by modeling the trustworthiness (competency and willingness) of advisers in reporting seller information; (2) discover sellers who maximize their profit by modeling the trustworthiness of sellers and considering the buyers’ preferences on product quality. Experimental results confirm that competitive marketplaces operating with our mechanism lead to better profit for buyers and create incentives for seller honesty.
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    ABSTRACT: With the proliferation of online communities, the deployment of knowledge, skills, experiences and user generated content are generally facilitated among participant users. In online social media-sharing communities, the success of social interactions for content sharing and dissemination among completely unknown users depends on ‘trust’. Therefore, providing a satisfactory trust model to evaluate the quality of content and to recommend personalized trustworthy content providers is vital for a successful online social media-sharing community. Current research on trust prediction strongly relies on a web of trust, which is directly collected from users. However, the web of trust is not always available in online communities and, even when it is available, it is often too sparse to accurately predict the trust value between two unacquainted people. Moreover, most of the extant trust research studies have not paid attention to the importance of distrust, even though distrust is a distinct concept from trust with different impacts on behavior. In this paper, we adopt the concepts of ‘trust’, ‘distrust’, and ‘lack of confidence’ in social relationships and propose a novel unifying framework to predict trust and distrust as well as to distinguish the confidently-made decisions (trust or distrust) from lack of confidence without a web of trust. This approach uses interaction histories among users including rating data that is available and much denser than explicit trust/distrust statements (i.e. a web of trust).
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    ABSTRACT: Frameworks for aggregating similar services into structures called communities have been recently advocated. A common assumption in those frameworks is that residing services are coopetitive, i.e., competing over received requests, but also cooperating, for instance in terms of substituting each other. In this coopetition context, deciding to compete or cooperate at different moments in time is an open question yet to be addressed. The contribution of this paper is the answer to this challenging question by proposing a game-theoretic-based decision mechanism that services can use to effectively choose competition or cooperation strategies that maximize their payoffs. To achieve this objective, we investigate autonomous services’ characteristics and their expected utilities over different strategies. We propose a game-theoretic best response technique to measure the threshold that services can use in order to decide about the two strategies. We prove that the proposed decision mechanism is efficient and can be implemented in time linear in the length of the time period considered for the analysis and the number of services in the community. Moreover, we conduct extensive simulations to analyze various scenarios and confirm the obtained theoretical results. Those results show that our model outperforms existing competitive and random coopetitive strategies and the more services deviate from our game-theoretic-based coopetitive strategy the more they make less benefits.
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