Reinforcement learning of normative monitoring intensities
Choosing actions within norm-regulated environments involves balancing achieving one’s goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in envir...
| Main Authors: | , , , |
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| Format: | Article |
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Springer Verlag
2016
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| Online Access: | https://eprints.nottingham.ac.uk/46971/ |
| _version_ | 1848797439521193984 |
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| author | Li, Jiaqi Meneguzzi, Felipe Fagundes, Moser Logan, Brian |
| author_facet | Li, Jiaqi Meneguzzi, Felipe Fagundes, Moser Logan, Brian |
| author_sort | Li, Jiaqi |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Choosing actions within norm-regulated environments involves balancing achieving one’s goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in environments where there is uncertainty regarding both the outcomes of agent actions and the intensity of monitoring for norm violations. Our technique assumes no prior knowledge of probabilities over action outcomes or the likelihood of norm violations being detected by employing reinforcement learning to discover both the dynamics of the environment and the effectiveness of the enforcer. Results indicate agents become aware of greater rewards for violations when enforcement is lax, which gradually become less attractive as the enforcement is increased. |
| first_indexed | 2025-11-14T20:03:54Z |
| format | Article |
| id | nottingham-46971 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:03:54Z |
| publishDate | 2016 |
| publisher | Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-469712020-05-04T18:01:44Z https://eprints.nottingham.ac.uk/46971/ Reinforcement learning of normative monitoring intensities Li, Jiaqi Meneguzzi, Felipe Fagundes, Moser Logan, Brian Choosing actions within norm-regulated environments involves balancing achieving one’s goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in environments where there is uncertainty regarding both the outcomes of agent actions and the intensity of monitoring for norm violations. Our technique assumes no prior knowledge of probabilities over action outcomes or the likelihood of norm violations being detected by employing reinforcement learning to discover both the dynamics of the environment and the effectiveness of the enforcer. Results indicate agents become aware of greater rewards for violations when enforcement is lax, which gradually become less attractive as the enforcement is increased. Springer Verlag 2016-07-13 Article PeerReviewed Li, Jiaqi, Meneguzzi, Felipe, Fagundes, Moser and Logan, Brian (2016) Reinforcement learning of normative monitoring intensities. Lecture Notes in Computer Science, 9628 . pp. 209-223. ISSN 0302-9743 https://link.springer.com/chapter/10.1007/978-3-319-42691-4_12 doi:10.1007/978-3-319-42691-4_12 doi:10.1007/978-3-319-42691-4_12 |
| spellingShingle | Li, Jiaqi Meneguzzi, Felipe Fagundes, Moser Logan, Brian Reinforcement learning of normative monitoring intensities |
| title | Reinforcement learning of normative monitoring intensities |
| title_full | Reinforcement learning of normative monitoring intensities |
| title_fullStr | Reinforcement learning of normative monitoring intensities |
| title_full_unstemmed | Reinforcement learning of normative monitoring intensities |
| title_short | Reinforcement learning of normative monitoring intensities |
| title_sort | reinforcement learning of normative monitoring intensities |
| url | https://eprints.nottingham.ac.uk/46971/ https://eprints.nottingham.ac.uk/46971/ https://eprints.nottingham.ac.uk/46971/ |