The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Machine Learning, Functions and Goals
Open accessAbstract
Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs.
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“Reinforcement learning (RL) research explicitly aims to build artificial agents which pursue goals (Sutton & Barto 2018), and typical RL systems meet all three of these conditions.”
From Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (2023) · cites this paper as Butlin 2022
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Language, Thought, and Other Biological Categories: New Foundations for Realism
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
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Cited by
Sorted by citations
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., Deane, G., Fleming, S. M., Frith, C., Ji, X., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.08708 · Cited by 196
Self-citationUnderstanding artificial agency
Dung, L. (2025) · The Philosophical Quarterly · doi:10.1093/pq/pqae010 · Cited by 69
Reinforcement learning and artificial agency
Butlin, P. (2024) · Mind & Language · doi:10.1111/mila.12458 · Cited by 40
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Metadata
- Authors
- Butlin, P.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Butlin, Patrick
- ORCID
- Butlin, Patrick
- Venue
- Croatian journal of philosophy
- Published
- 2022-12-27
- Volume
- 22
- Issue
- 66
- Pages
- 351-370
- ISSN
- 1333-1108 (ISSN-L)
- 1847-6139
- OpenAlex
- W4313412039
- Cited by
- 22
- References
- 5
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 15 · 2023: 1
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Croatian Journal of Philosophy (publishedVersion)
- University of Zagreb University Computing Centre (SRCE) (submittedVersion, other-oa)
- Topics
- Social Sciences › Arts and Humanities › Philosophy › Philosophy and Theoretical Science
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- machine learning, agency, reinforcement learning, supervised learning, instrumental values, functions, goals, artificial agents
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