The ATTUNE Model for Artificial Trust Towards Human Operators
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| Název: | The ATTUNE Model for Artificial Trust Towards Human Operators |
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| Autoři: | Petousakis, Giannis, Cangelosi, Angelo, Stolkin, Rustam, Chiou, Manolis |
| Zdroj: | Petousakis, G, Cangelosi, A, Stolkin, R & Chiou, M 2025, The ATTUNE Model for Artificial Trust Towards Human Operators. in 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC) : Proceedings. IEEE, pp. 1-8, 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia, 6/10/24. https://doi.org/10.1109/SMC54092.2024.10831621 |
| Publication Status: | Preprint |
| Informace o vydavateli: | IEEE, 2024. |
| Rok vydání: | 2024 |
| Témata: | FOS: Computer and information sciences, Computer Science - Robotics, real time systems, monitoring and evaluation, robot awareness, human-robot collaboration, artificial intelligence (incl. Robotics), Robotics (cs.RO), navigation and exploration |
| Popis: | This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the framework is based on principles drawn from Theory of Mind, including information about the human state, action, and intent. The framework creates the ATTUNE model for Artificial Trust Towards Human Operators. The model uses metrics on the operator's state of attention, navigational intent, actions, and performance to quantify the Trust towards them. The model is tested on a pre-existing dataset that includes recordings (ROSbags) of a human trial in a simulated disaster response scenario. The performance of ATTUNE is evaluated through a qualitative and quantitative analysis. The results of the analyses provide insight into the next stages of the research and help refine the proposed approach. Published in IEEE SMC 2024 |
| Druh dokumentu: | Article Conference object Contribution for newspaper or weekly magazine |
| DOI: | 10.1109/smc54092.2024.10831621 |
| DOI: | 10.48550/arxiv.2411.19580 |
| Přístupová URL adresa: | http://arxiv.org/abs/2411.19580 https://research.manchester.ac.uk/en/publications/1a2643db-3786-467e-8f46-564734e6681f https://doi.org/10.1109/SMC54092.2024.10831621 https://research.manchester.ac.uk/en/publications/1a2643db-3786-467e-8f46-564734e6681f https://doi.org/10.1109/SMC54092.2024.10831621 https://doi.org/10.48550/arXiv.2411.19580 |
| Rights: | STM Policy #29 CC BY NC SA CC BY |
| Přístupové číslo: | edsair.doi.dedup.....8c22403cc26750e80f35c835c16a63ee |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the framework is based on principles drawn from Theory of Mind, including information about the human state, action, and intent. The framework creates the ATTUNE model for Artificial Trust Towards Human Operators. The model uses metrics on the operator's state of attention, navigational intent, actions, and performance to quantify the Trust towards them. The model is tested on a pre-existing dataset that includes recordings (ROSbags) of a human trial in a simulated disaster response scenario. The performance of ATTUNE is evaluated through a qualitative and quantitative analysis. The results of the analyses provide insight into the next stages of the research and help refine the proposed approach.<br />Published in IEEE SMC 2024 |
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| DOI: | 10.1109/smc54092.2024.10831621 |