Analysing Explanation-Related Interactions in Collaborative Perception-Cognition-Communication-Action
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917841327882240 |
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| author | Vilamala, Marc Roig Furby, Jack Briseno, Julian de Gortari Srivastava, Mani Preece, Alun Toro, Carolina Fuentes |
| author_facet | Vilamala, Marc Roig Furby, Jack Briseno, Julian de Gortari Srivastava, Mani Preece, Alun Toro, Carolina Fuentes |
| contents | Effective communication is essential in collaborative tasks, so AI-equipped robots working alongside humans need to be able to explain their behaviour in order to cooperate effectively and earn trust. We analyse and classify communications among human participants collaborating to complete a simulated emergency response task. The analysis identifies messages that relate to various kinds of interactive explanations identified in the explainable AI literature. This allows us to understand what type of explanations humans expect from their teammates in such settings, and thus where AI-equipped robots most need explanation capabilities. We find that most explanation-related messages seek clarification in the decisions or actions taken. We also confirm that messages have an impact on the performance of our simulated task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12483 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysing Explanation-Related Interactions in Collaborative Perception-Cognition-Communication-Action Vilamala, Marc Roig Furby, Jack Briseno, Julian de Gortari Srivastava, Mani Preece, Alun Toro, Carolina Fuentes Human-Computer Interaction Artificial Intelligence Computation and Language Effective communication is essential in collaborative tasks, so AI-equipped robots working alongside humans need to be able to explain their behaviour in order to cooperate effectively and earn trust. We analyse and classify communications among human participants collaborating to complete a simulated emergency response task. The analysis identifies messages that relate to various kinds of interactive explanations identified in the explainable AI literature. This allows us to understand what type of explanations humans expect from their teammates in such settings, and thus where AI-equipped robots most need explanation capabilities. We find that most explanation-related messages seek clarification in the decisions or actions taken. We also confirm that messages have an impact on the performance of our simulated task. |
| title | Analysing Explanation-Related Interactions in Collaborative Perception-Cognition-Communication-Action |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2411.12483 |