Trust and deception in virtual societies
An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
An integrated trust and reputation model for open multi-agent systems
Autonomous Agents and Multi-Agent Systems
Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links
International Journal of Human-Computer Studies
Dynamically learning sources of trust information: experience vs. reputation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Trust transferability among similar contexts
Proceedings of the 4th ACM symposium on QoS and security for wireless and mobile networks
Bootstrapping trust evaluations through stereotypes
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Trust Theory: A Socio-Cognitive and Computational Model
Trust Theory: A Socio-Cognitive and Computational Model
Trust model architecture: defining prejudice by learning
TrustBus'06 Proceedings of the Third international conference on Trust, Privacy, and Security in Digital Business
The impact of benevolence in computational trust
AT'13 Proceedings of the Second international conference on Agreement Technologies
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Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experiences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed membership to categories; (2) learn a series of emergent relations between trustees observable properties and their effective abilities to fulfill tasks in situated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of architectures combining categorization abilities, individual experiences and context awareness are evaluated and compared in simulated experiments.