Name
Abstract | ||
|---|---|---|
State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world dataset, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TrustCom.2013.5 | TrustCom/ISPA/IUCC |
Keywords | Field | DocType |
complex adaptive system,supervised method,trustworthiness assessment,robust probabilistic trust assessment,trust assessment methodology,estimator output,experience-based bayesian trust assessment,supervised learning,supervised machine,belief logic representation,reputation-based service selection,state-of-the art trust,computational modeling,machine learning,trusted computing,estimation,learning artificial intelligence,predictive models,data models,vegetation | Generalizability theory,Data modeling,Trusted Computing,Computer science,Supervised learning,Artificial intelligence,Probabilistic logic,Complex adaptive system,Machine learning,Reputation,Bayesian probability | Conference |
ISSN | Citations | PageRank |
2324-898X | 4 | 0.40 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sascha Hauke | 1 | 68 | 8.15 |
| Sebastian Biedermann | 2 | 82 | 7.98 |
| Max Mühlhäuser | 3 | 1652 | 252.87 |
| Dominik Heider | 4 | 173 | 17.90 |