Problem Solving Through Human-AI Preference-Based Cooperation
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909660916744192 |
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| author | Dutta, Subhabrata Kaufmann, Timo Glavaš, Goran Habernal, Ivan Kersting, Kristian Kreuter, Frauke Mezini, Mira Gurevych, Iryna Hüllermeier, Eyke Schuetze, Hinrich |
| author_facet | Dutta, Subhabrata Kaufmann, Timo Glavaš, Goran Habernal, Ivan Kersting, Kristian Kreuter, Frauke Mezini, Mira Gurevych, Iryna Hüllermeier, Eyke Schuetze, Hinrich |
| contents | While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human-AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty to keep track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAICo2, a novel human-AI co-construction framework. We take first steps towards a formalization of HAICo2 and discuss the difficult open research problems that it faces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07461 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Problem Solving Through Human-AI Preference-Based Cooperation Dutta, Subhabrata Kaufmann, Timo Glavaš, Goran Habernal, Ivan Kersting, Kristian Kreuter, Frauke Mezini, Mira Gurevych, Iryna Hüllermeier, Eyke Schuetze, Hinrich Artificial Intelligence Human-Computer Interaction While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human-AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty to keep track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAICo2, a novel human-AI co-construction framework. We take first steps towards a formalization of HAICo2 and discuss the difficult open research problems that it faces. |
| title | Problem Solving Through Human-AI Preference-Based Cooperation |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.07461 |