Saved in:
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.04403 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913681850236928 |
|---|---|
| author | Abel, David Barreto, André Bowling, Michael Dabney, Will Dong, Shi Hansen, Steven Harutyunyan, Anna Khetarpal, Khimya Lyle, Clare Pascanu, Razvan Piliouras, Georgios Precup, Doina Richens, Jonathan Rowland, Mark Schaul, Tom Singh, Satinder |
| author_facet | Abel, David Barreto, André Bowling, Michael Dabney, Will Dong, Shi Hansen, Steven Harutyunyan, Anna Khetarpal, Khimya Lyle, Clare Pascanu, Razvan Piliouras, Georgios Precup, Doina Richens, Jonathan Rowland, Mark Schaul, Tom Singh, Satinder |
| contents | Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agency Is Frame-Dependent Abel, David Barreto, André Bowling, Michael Dabney, Will Dong, Shi Hansen, Steven Harutyunyan, Anna Khetarpal, Khimya Lyle, Clare Pascanu, Razvan Piliouras, Georgios Precup, Doina Richens, Jonathan Rowland, Mark Schaul, Tom Singh, Satinder Artificial Intelligence Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning. |
| title | Agency Is Frame-Dependent |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.04403 |