Path-Specific Objectives for Safer Agent Incentives Article Swipe
Sebastian Farquhar
,
Ryan M. Carey
,
Tom Everitt
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v36i9.21186
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v36i9.21186
We present a general framework for training safe agents whose naive incentives are unsafe. As an example, manipulative or deceptive behaviour can improve rewards but should be avoided. Most approaches fail here: agents maximize expected return by any means necessary. We formally describe settings with `delicate' parts of the state which should not be used as a means to an end. We then train agents to maximize the causal effect of actions on the expected return which is not mediated by the delicate parts of state, using Causal Influence Diagram analysis. The resulting agents have no incentive to control the delicate state. We further show how our framework unifies and generalizes existing proposals.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v36i9.21186
- https://ojs.aaai.org/index.php/AAAI/article/download/21186/20935
- OA Status
- diamond
- Cited By
- 9
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283790836
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283790836Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v36i9.21186Digital Object Identifier
- Title
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Path-Specific Objectives for Safer Agent IncentivesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-06-28Full publication date if available
- Authors
-
Sebastian Farquhar, Ryan M. Carey, Tom EverittList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v36i9.21186Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/21186/20935Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/21186/20935Direct OA link when available
- Concepts
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Incentive, SAFER, Computer science, Path (computing), Risk analysis (engineering), State (computer science), Control (management), Operations research, Microeconomics, Computer security, Business, Artificial intelligence, Economics, Engineering, Algorithm, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.can | 21 |
| abstract_inverted_index.for | 5 |
| abstract_inverted_index.how | 105 |
| abstract_inverted_index.not | 52, 78 |
| abstract_inverted_index.our | 106 |
| abstract_inverted_index.the | 48, 67, 73, 81, 99 |
| abstract_inverted_index.Most | 28 |
| abstract_inverted_index.end. | 60 |
| abstract_inverted_index.fail | 30 |
| abstract_inverted_index.have | 94 |
| abstract_inverted_index.safe | 7 |
| abstract_inverted_index.show | 104 |
| abstract_inverted_index.then | 62 |
| abstract_inverted_index.used | 54 |
| abstract_inverted_index.with | 44 |
| abstract_inverted_index.here: | 31 |
| abstract_inverted_index.means | 38, 57 |
| abstract_inverted_index.naive | 10 |
| abstract_inverted_index.parts | 46, 83 |
| abstract_inverted_index.state | 49 |
| abstract_inverted_index.train | 63 |
| abstract_inverted_index.using | 86 |
| abstract_inverted_index.which | 50, 76 |
| abstract_inverted_index.whose | 9 |
| abstract_inverted_index.Causal | 87 |
| abstract_inverted_index.agents | 8, 32, 64, 93 |
| abstract_inverted_index.causal | 68 |
| abstract_inverted_index.effect | 69 |
| abstract_inverted_index.return | 35, 75 |
| abstract_inverted_index.should | 25, 51 |
| abstract_inverted_index.state, | 85 |
| abstract_inverted_index.state. | 101 |
| abstract_inverted_index.Diagram | 89 |
| abstract_inverted_index.actions | 71 |
| abstract_inverted_index.control | 98 |
| abstract_inverted_index.further | 103 |
| abstract_inverted_index.general | 3 |
| abstract_inverted_index.improve | 22 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.rewards | 23 |
| abstract_inverted_index.unifies | 108 |
| abstract_inverted_index.unsafe. | 13 |
| abstract_inverted_index.avoided. | 27 |
| abstract_inverted_index.delicate | 82, 100 |
| abstract_inverted_index.describe | 42 |
| abstract_inverted_index.example, | 16 |
| abstract_inverted_index.existing | 111 |
| abstract_inverted_index.expected | 34, 74 |
| abstract_inverted_index.formally | 41 |
| abstract_inverted_index.maximize | 33, 66 |
| abstract_inverted_index.mediated | 79 |
| abstract_inverted_index.settings | 43 |
| abstract_inverted_index.training | 6 |
| abstract_inverted_index.Influence | 88 |
| abstract_inverted_index.analysis. | 90 |
| abstract_inverted_index.behaviour | 20 |
| abstract_inverted_index.deceptive | 19 |
| abstract_inverted_index.framework | 4, 107 |
| abstract_inverted_index.incentive | 96 |
| abstract_inverted_index.resulting | 92 |
| abstract_inverted_index.`delicate' | 45 |
| abstract_inverted_index.approaches | 29 |
| abstract_inverted_index.incentives | 11 |
| abstract_inverted_index.necessary. | 39 |
| abstract_inverted_index.proposals. | 112 |
| abstract_inverted_index.generalizes | 110 |
| abstract_inverted_index.manipulative | 17 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.76716611 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |