A Learning Automata Based Solution to Service Selection in Stochastic Environments

With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based on Learning Automata (LA), which can learn the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In additional to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems with reputation systems. Instead, it gradually learns which users provide fair ratings, and which users provide unfair ratings, even when users unintentionally make mistakes. Comprehensive empirical results show that our LA based scheme efficiently handles any degree of unfair ratings (as long as ratings are binary). Furthermore, if the quality of services and/or the trustworthiness of users change, our scheme is able to robustly track such changes over time. Finally, the scheme is ideal for decentralized processing. Accordingly, we believe that our LA based scheme forms a promising basis for improving the performance of reputation systems in general

A user-centric approach for personalized service provisioning in pervasive environments

Published version of an article published in Wireless Personal Communications (2011). Also available from the publisher at http://dx.doi.org/10.1007/s11277-011-0387-3The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention of the user is becoming an increasingly-challenging task. In this paper, we present an adaptive multi-criteria decision making mechanism for recommending relevant services to the mobile user. In this context, "Relevance" is determined based on a user-centric approach that combines both the reputation of the service, the user's current context, the user's profile, as well as a record of the history of recommendations. Our decision making mechanism is adaptive in the sense that it is able to cope with users' contexts that are changing and drifts in the users' interests, while it simultaneously can track the reputations of services, and suppress repetitive notifications based on the history of the recommendations. The paper also includes some brief but comprehensive results concerning the task of tracking service reputations by analyzing and comprehending Word-of-Mouth communications, as well as by suppressing repetitive notifications. We believe that our architecture presents a significant contribution towards realizing intelligent and personalized service provisioning in pervasive environments