Machine Learning
Agent Mediated Electronic Commerce, The European AgentLink Perspective.
Formal Analysis of Models for the Dynamics of Trust Based on Experiences
MAAMAW '99 Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: MultiAgent System Engineering
Multidimensional Context Representations for Situational Trust
DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
On representation and aggregation of social evaluations in computational trust and reputation models
International Journal of Approximate Reasoning
Electronic institutions for B2B: dynamic normative environments
Artificial Intelligence and Law
Computing Confidence Values: Does Trust Dynamics Matter?
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Engineering open environments with electronic institutions
Engineering Applications of Artificial Intelligence
Monitoring directed obligations with flexible deadlines: a rule-based approach
DALT'09 Proceedings of the 7th international conference on Declarative Agent Languages and Technologies
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An Electronic Institution includes a normative environment with rules and norms for agents' interoperability, and is also a service providing platform that assists agents in the task of establishing and conducting normative relationships (contracts). Using this platform, agents representing organizations willing to engage in a collective contractual activity select partners according to different factors, including their capabilities, current business needs and information on past business experiences that may be used as inputs to trust building. In our framework we have designed a tightly coupled connection between electronic contract monitoring and a computational trust model. In this paper, we explain the rationale behind this connection and detail how it is materialized. In particular, we explain how our situation-aware trust model relies on past contractual behavior to dynamically build up a trustworthiness image of each agent that can be helpful for future encounters. Experiments with simplified scenarios show the effectiveness of our approach.