Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Learning automata: an introduction
Learning automata: an introduction
Improvements to an Algorithm for Equipartitioning
IEEE Transactions on Computers
The weighted majority algorithm
Information and Computation
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Computing and using reputations for internet ratings
Proceedings of the 3rd ACM conference on Electronic Commerce
Robustness of reputation-based trust: boolean case
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Combining Intelligent Techniques for Sensor Fusion
Applied Intelligence
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
A survey of trust and reputation systems for online service provision
Decision Support Systems
Analysis of a reputation system for Mobile Ad-Hoc Networks with liars
Performance Evaluation
Fraudulent and malicious sites on the web
Applied Intelligence
Fusion of imprecise qualitative information
Applied Intelligence
Applied Intelligence
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized pursuit learning schemes: new families of continuous and discretized learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments
Wireless Personal Communications: An International Journal
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In this paper, we propose a novel solution to the problem of identifying services of high quality. The reported solutions to this problem have, in one way or the other, resorted to using so-called "Reputation Systems" (RSs). Although these systems can offer generic recommendations by aggregating user-provided opinions about the quality of the services under consideration, they are, understandably, prone to "ballot stuffing" and "badmouthing" in a competitive marketplace. In general, unfair ratings may degrade the trustworthiness of RSs, and additionally, changes in the quality of service, over time, can render previous ratings unreliable. As opposed to the reported solutions, in this paper, we propose to solve the problem using tools provided by Learning Automata (LA), which have proven properties capable of learning the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In addition 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 associated with RSs. Instead, it gradually learns the identity and characteristics of the users which provide fair ratings, and of those who provide unfair ratings, even when these are a consequence of them making unintentional mistakes.Comprehensive empirical results show that our LA-based scheme efficiently handles any degree of unfair ratings (as long as these ratings are binary--the extension to non-binary ratings is "trivial", if we use the S-model of LA computations instead of the P-model). Furthermore, if the quality of services and/or the trustworthiness of the 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 RSs in general.