Trust between humans and machines, and the design of decision aids
International Journal of Man-Machine Studies - Special Issue: Cognitive Engineering in Dynamic Worlds
Trust, self-confidence, and operators' adaptation to automation
International Journal of Human-Computer Studies
Does automation bias decision-making?
International Journal of Human-Computer Studies
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
Moticons: detection, distraction and task
International Journal of Human-Computer Studies - Notification user interfaces
Principles of Trust for MAS: Cognitive Anatomy, Social Importance, and Quantification
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Aiding Human Reliance Decision Making Using Computational Models of Trust
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Modeling Dynamics of Relative Trust of Competitive Information Agents
CIA '08 Proceedings of the 12th international workshop on Cooperative Information Agents XII
A model for types and levels of human interaction with automation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Problems with estimating trust in information sources are common in time constraining and ambiguous situations and often lead to a decrease of team performance. Humans lack the resources to track the integrity of information and thus tend to over- or under-rely on advice from support systems. Two types of adaptive team support have been developed and evaluated that are intended to support human-computer teams in estimating trust appropriately and making appropriate reliance decisions thereof. The first adaptive system (graphical support) supports by communicating the estimated degree of over- or under-trust. The second system (adaptive autonomy) takes over a reliance decision when this estimation exceeds a certain threshold. The two types of support were implemented in a multi-agent environment where human operators and Unmanned Aerial Vehicles (UAVs) work together on a target classification task. We evaluated the two support types in terms of team performance, satisfaction and effectiveness and obtained promising results.