Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning | Paper Citation Record

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This paper cites In: Proceed- ings of the Twenty-First International Conference on Machine Learning.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceed- ings of the Twenty-First International Conference on Machine Learning

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This paper cites Constitutional AI: Harmlessness from AI Feedback.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Constitutional AI: Harmlessness from AI Feedback

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This paper cites The American Economic Review97, 170–176 (2007).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning The American Economic Review97, 170–176 (2007)

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This paper cites The American Economic Review 90(1), 166–193 (2000).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning The American Economic Review 90(1), 166–193 (2000)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Unresolved cited work

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This paper cites Exploration by Random Network Distillation.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Exploration by Random Network Distillation

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This paper cites Neuron70(3), 560–572 (May 2011).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Neuron70(3), 560–572 (May 2011)

Reference 7

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This paper cites Econometrica 74(6), 1579–1601 (2006).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Econometrica 74(6), 1579–1601 (2006)

Reference 8

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This paper cites The Quarterly Journal of Economics117(3), 817–869 (2002).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning The Quarterly Journal of Economics117(3), 817–869 (2002)

Reference 9

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This paper cites In: Proceedings of the 31st International Conference on Neural Information Processing Systems.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 31st International Conference on Neural Information Processing Systems

Reference 10

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This paper cites Open Problems in Cooperative AI.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Open Problems in Cooperative AI

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This paper cites Learning Reciprocity in Complex Sequential Social Dilemmas.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Learning Reciprocity in Complex Sequential Social Dilemmas

Reference 12

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This paper cites Nature protocols15, 2186 – 2202 (2019).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Nature protocols15, 2186 – 2202 (2019)

Reference 13

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This paper cites Diversity is All You Need: Learning Skills without a Reward Function.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Diversity is All You Need: Learning Skills without a Reward Function

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Munich Reprints in Economics 4 (1998)

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This paper cites In: Adaptive Agents and Multi-Agent Systems (2017).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Adaptive Agents and Multi-Agent Systems (2017)

Reference 16

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This paper cites eLife 14 (Mar 2026).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning eLife 14 (Mar 2026)

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This paper cites Scientific Data3(1) (2016).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Scientific Data3(1) (2016)

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This paper cites In: Neural Information Processing Systems (2016).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Neural Information Processing Systems (2016)

Reference 19

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This paper cites Neuron 95(2), 245–258 (2017).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Neuron 95(2), 245–258 (2017)

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This paper cites Autonomous Agents and Multi-Agent Systems33(6), 750–797 (Nov 2019).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Autonomous Agents and Multi-Agent Systems33(6), 750–797 (Nov 2019)

Reference 21

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This paper cites In: Proceedings of the 32nd International Conference on Neural Information Processing Systems.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 32nd International Conference on Neural Information Processing Systems

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This paper cites In: International Conference on Machine Learning (2018).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: International Conference on Machine Learning (2018)

Reference 23

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This paper cites Journal of Cognitive Neuroscience25, 258–272 (2013).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Journal of Cognitive Neuroscience25, 258–272 (2013)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Unresolved cited work

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This paper cites In: Proceedings of the 31st International Conference on Neural Information Processing Systems.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 31st International Conference on Neural Information Processing Systems

Reference 26

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This paper cites In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Scalable agent alignment via reward modeling: a research direction

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Maintaining cooperation in complex social dilemmas using deep reinforcement learning

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Frontiers in Computational Neuroscience10 (2016)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning eLife10(Oct 2021)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Proceedings of the Na- tional Academy of Sciences103, 15623 – 15628 (2006)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: International Conference on Machine Learning (1999)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: International Confer- ence on Machine Learning (2000)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 36th International Conference on Neural Information Processing Systems

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 31st International Conference on Neural Information Processing Systems

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning The American Economic Review 83(5), 1281–1302 (1993)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Unresolved cited work

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Neural Comput

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This paper cites Proceedings of the National Academy of Sciences111(33), 12252–12257 (2014).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Proceedings of the National Academy of Sciences111(33), 12252–12257 (2014)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Proximal Policy Optimization Algorithms

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning https://doi.org/10.18112/ OPENNEURO.DS005588.V1.0.1

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

Reference 46

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Unresolved cited work

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Unresolved cited work

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This paper cites Nature575, 350 – 354 (2019).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Nature575, 350 – 354 (2019)

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This paper cites Cerebral Cortex21(11), 2461–2470 (Mar 2011).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Cerebral Cortex21(11), 2461–2470 (Mar 2011)

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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Unresolved cited work

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Observation 816361bf-2898-4bbc-bcc3-17661e607241 · outbound

This paper cites Nature Neuroscience 21(6), 860–868 (May 2018).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Nature Neuroscience 21(6), 860–868 (May 2018)

Reference 52

Resolution: verified exact; doi, observed 2026-08-06T19:33:43.509030Z

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Observation 563f8482-0866-4d17-b6bc-a22fe52700dd · outbound

This paper cites an unresolved cited work.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:33:45.457836Z

Source-reported events for the cited work

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Observation e10a2bcd-64f3-4eda-9170-3b2d57b13ca3 · outbound

This paper cites In: Proceedings of the 36th International Conference on Neural Information Processing Systems.

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning In: Proceedings of the 36th International Conference on Neural Information Processing Systems

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:33:45.174240Z

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Observation eefb90eb-b513-45a3-8d1b-3ce2674b16f9 · outbound

This paper cites Social Cognitive and Affective Neuroscience9(8), 1150–1158 (Aug 2013).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Social Cognitive and Affective Neuroscience9(8), 1150–1158 (Aug 2013)

Reference 55

Resolution: verified exact; doi, observed 2026-08-06T19:33:43.191975Z

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Observation 12cac709-ecbc-45e6-85d1-03afa278073e · outbound

This paper cites Cerebral Cortex19(2), 276–283 (May 2008).

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning Cerebral Cortex19(2), 276–283 (May 2008)

Reference 56

Resolution: verified exact; doi, observed 2026-08-06T19:33:42.894261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Reference resolution

Outbound references

Pith citing papers