Journal ArticleDOI
Trust management framework for intelligent agent negotiations in ubiquitous computing environments
Reads0
Chats0
TLDR
A reputation mechanism is proposed which helps estimating SRPs trustworthiness and predicting their future behaviour, taking into account their past performance in consistently satisfying SRRs’ expectations, while it exhibits a robust behaviour against inaccurate reputation ratings.Abstract:
In dynamic ubiquitous computing environments, system entities may be classified into two main categories that are, in principle, in conflict. These are the Service Resource Requestors (SRRs) wishing to use services and/or exploit resources offered by the other system entities and the Service Resource Providers (SRPs) that offer the services/resources requested. Seeking for the maximisation of their welfare, while achieving their own goals and aims, entities may misbehave (intentionally or unintentionally), thus, leading to a significant deterioration of system's performance. In this study, a reputation mechanism is proposed which helps estimating SRPs trustworthiness and predicting their future behaviour, taking into account their past performance in consistently satisfying SRRs' expectations. Thereafter, under the assumption that a number of SRPs may handle the SRRs requests, the SRRs may decide on the most appropriate SRP for the service/resource requested on the basis of a weighted combination of the evaluation of the quality of their offer (performance related factor) and of their reputation rating (reliability related factor). The proposed trust management framework is distributed, considers both first-hand information (acquired from the SRR's direct past experiences with the SRPs) and second-hand information (disseminated from other SRRs' past experiences with the SRPs), while it exhibits a robust behaviour against inaccurate reputation ratings. The designed mechanisms have been empirically evaluated simulating interactions among self-interested agents, exhibiting improved performance with respect to random SRP selection.read more
Citations
More filters
Journal ArticleDOI
A Robust Reputation-Based Computational Model for Trust Establishment in Pervasive Systems
Stylianos Kraounakis,Ioannis N. Demetropoulos,Angelos Michalas,Mohammad S. Obaidat,Panagiotis Sarigiannidis,Malamati Louta +5 more
TL;DR: A computational model for trust establishment based on a reputation mechanism, which incorporates direct SRRs' experiences and information disseminated from witness SRRs on the basis of their past experiences with SRPs, which exhibits good performance.
Journal ArticleDOI
A Low Computational-Cost Electronic Payment Scheme for Mobile Commerce with Large-Scale Mobile Users
Jen-Ho Yang,Chin-Chen Chang +1 more
TL;DR: A low computation-cost electronic payment scheme for mobile commerce that can be applied to large-scale mobile user environments without maintaining a large authentication table and has low computation loads for mobile users because the elliptic curve cryptography is adopted in the proposed scheme.
Proceedings ArticleDOI
The Competitor Busting Strategy in Keyword Auctions: Who's Worst Hit?
TL;DR: It is shown that the Competitor Busting strategy is largely ineffective and the lifetime of non-aggressive bidders, their presence in the auction, and the proportion of slots they are awarded are not affected by the presence of aggressive bidder.
References
More filters
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Posted Content
Reinforcement Learning: A Survey
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Book
The art and science of negotiation
TL;DR: In this article, the authors present an overview of the history of the Panama Canal Negotiations and discuss the role of time, risk sharing, and third-party intervention in these negotiations.