Situation-Aware Computational Trust Model for Selecting Partners

• Type: Article in International Scientific Journal
• Date: 2011

Authors

Joana Urbano (Author) FEUP; Ana Paula Cunha da Rocha (Author) FEUP; Eugénio Oliveira (Author) FEUP

Journal

Title: Transactions on Computational Collective Intelligence

No. V

Pages: 84-105

ISSN: 2190-9288

Publisher: Springer

Indexing

ISI Proceedings

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Informatics

Other information

Resumo (PT): Trust estimation is a fundamental process in several multi- agent systems domains, from social networks to electronic business scenarios. However, the majority of current computational trust systems is still too simplistic and is not situation-aware, jeopardizing the accuracy of the predicted trustworthiness values of agents. In this paper, we address the inclusion of context in the trust management process. We first overview recently proposed situation-aware trust models, all based on the predefinition of similarity measures between situations. Then, we present our computational trust model, and we focus on Contextual Fitness, a component of the model that adds a contextual dimensional to existing trust aggregation engines. This is a dynamic and incremental technique that extracts tendencies of behavior from the agents in evaluation and that does not imply the predefinition of similarity measures between contexts. Finally, we evaluate our trust model and compare it with other trust approaches in an agent-based, open market trading simulation scenario. The results obtained show that our dynamic and incremental technique outperforms the other approaches in open and dynamic environments. By analyzing examples derived from the experiments, we show why our technique get better results than situation-aware trust models that are based on pre-defined similarity measures.

Abstract (EN): Trust estimation is a fundamental process in several multi- agent systems domains, from social networks to electronic business scenarios. However, the majority of current computational trust systems is still too simplistic and is not situation-aware, jeopardizing the accuracy of the predicted trustworthiness values of agents. In this paper, we address the inclusion of context in the trust management process. We first overview recently proposed situation-aware trust models, all based on the predefinition of similarity measures between situations. Then, we present our computational trust model, and we focus on Contextual Fitness, a component of the model that adds a contextual dimensional to existing trust aggregation engines. This is a dynamic and incremental technique that extracts tendencies of behavior from the agents in evaluation and that does not imply the predefinition of similarity measures between contexts. Finally, we evaluate our trust model and compare it with other trust approaches in an agent-based, open market trading simulation scenario. The results obtained show that our dynamic and incremental technique outperforms the other approaches in open and dynamic environments. By analyzing examples derived from the experiments, we show why our technique get better results than situation-aware trust models that are based on pre-defined similarity measures.

Language: English

Type (Professor's evaluation): Scientific