Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi | Paper Citation Record

Observation 92762c7f-cfd6-4566-90b6-9a66e1eea83a · outbound

This paper cites (eds.) Multi-agent Reinforcement Learning: An Overview, pp.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi (eds.) Multi-agent Reinforcement Learning: An Overview, pp

Reference 1

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This paper cites IEEE Transactions on Industrial Informatics 9(1), 427–438 (2013) https://doi.org/10.1109/TII.2012.2219061.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi IEEE Transactions on Industrial Informatics 9(1), 427–438 (2013) https://doi.org/10.1109/TII.2012.2219061

Reference 2

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This paper cites Guided Deep Reinforcement Learning for Swarm Systems.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Guided Deep Reinforcement Learning for Swarm Systems

Reference 3

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This paper cites In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp

Reference 4

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This paper cites IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38(2), 156–172 (2008) https://doi.org/10.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38(2), 156–172 (2008) https://doi.org/10

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This paper cites PloS one 12(4), 0172395 (2017) https://doi.org/10.1371/journal.pone.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi PloS one 12(4), 0172395 (2017) https://doi.org/10.1371/journal.pone

Reference 6

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This paper cites AAAI/IAAI 1998(746-752), 2 (1998).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi AAAI/IAAI 1998(746-752), 2 (1998)

Reference 7

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This paper cites The Knowledge Engineering Review 27(1), 1–31 (2012) https://doi.org/10.1017/ S0269888912000057.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi The Knowledge Engineering Review 27(1), 1–31 (2012) https://doi.org/10.1017/ S0269888912000057

Reference 8

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This paper cites In: AAAI Fall Symposia, vol.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: AAAI Fall Symposia, vol

Reference 9

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This paper cites Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Value-Decomposition Networks For Cooperative Multi-Agent Learning

Reference 10

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This paper cites Journal of Machine Learning Research 21(178), 1–51 (2020).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Journal of Machine Learning Research 21(178), 1–51 (2020)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:22.088517Z

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This paper cites In: Advances in Neural Information Processing Systems, vol.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: Advances in Neural Information Processing Systems, vol

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:22.070196Z

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This paper cites In: International Conference on Learning Representations (2020).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: International Conference on Learning Representations (2020)

Reference 13

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This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 14

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This paper cites In: Icml, vol.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: Icml, vol

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This paper cites In: ICML, vol.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: ICML, vol

Reference 16

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This paper cites In: Tenth International Conference on Autonomous Agents and Multi-Agent Systems, pp.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: Tenth International Conference on Autonomous Agents and Multi-Agent Systems, pp

Reference 17

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Observation 4b5d8d0e-a862-45e2-9462-8f90046dfcad · outbound

This paper cites Reinforcement Learning with Unsupervised Auxiliary Tasks.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Reinforcement Learning with Unsupervised Auxiliary Tasks

Reference 18

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This paper cites Learning to Navigate in Complex Environments.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Learning to Navigate in Complex Environments

Reference 19

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This paper cites Artificial Intelligence 112(1), 181–211 (1999) https://doi.org/10.1016/S0004-3702(99)00052-1.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Artificial Intelligence 112(1), 181–211 (1999) https://doi.org/10.1016/S0004-3702(99)00052-1

Reference 20

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This paper cites Nature 529(7587), 484–489 (2016).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Nature 529(7587), 484–489 (2016)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.962960Z

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This paper cites Communications of the ACM 38(3), 58–68 (1995).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Communications of the ACM 38(3), 58–68 (1995)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.941826Z

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This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Dota 2 with Large Scale Deep Reinforcement Learning

Reference 23

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Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Unresolved cited work

Reference 24

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This paper cites Blackwell Publishers Ltd, Hoboken, New Jersey (2008).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Blackwell Publishers Ltd, Hoboken, New Jersey (2008)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.902098Z

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This paper cites In: International Conference on Machine Learning, pp.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: International Conference on Machine Learning, pp

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.881751Z

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This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 27

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This paper cites In: International Conference on Machine Learning, pp.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: International Conference on Machine Learning, pp

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.860207Z

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This paper cites In: Thirty-second AAAI Conference on Artificial Intelligence (2018).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: Thirty-second AAAI Conference on Artificial Intelligence (2018)

Reference 29

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This paper cites Energy 238, 121958 (2022) https://doi.org/10.1016/j.energy.2021.121958.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Energy 238, 121958 (2022) https://doi.org/10.1016/j.energy.2021.121958

Reference 30

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This paper cites International Journal of Electrical Power & Energy Systems 119, 105928 (2020) https://doi.org/10.1016/j.ijepes.2020.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi International Journal of Electrical Power & Energy Systems 119, 105928 (2020) https://doi.org/10.1016/j.ijepes.2020

Reference 31

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This paper cites Sustainability 14(3), 1884 (2022) https://doi.org/10.3390/su14031884.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Sustainability 14(3), 1884 (2022) https://doi.org/10.3390/su14031884

Reference 32

Resolution: verified exact; doi, observed 2026-08-11T19:51:20.438464Z

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This paper cites Applied Energy 318, 119151 (2022) https://doi.org/10.1016/j.apenergy.2022.119151.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Applied Energy 318, 119151 (2022) https://doi.org/10.1016/j.apenergy.2022.119151

Reference 33

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This paper cites On Multi-Agent Learning in Team Sports Games.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi On Multi-Agent Learning in Team Sports Games

Reference 34

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This paper cites In: International Conference on Machine Learning, pp.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: International Conference on Machine Learning, pp

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.837289Z

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This paper cites Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

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This paper cites Artificial Intelligence 280, 103216 (2020) https://doi.org/10.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Artificial Intelligence 280, 103216 (2020) https://doi.org/10

Reference 37

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This paper cites Mathematics Magazine 88(5), 323–336 (2015).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Mathematics Magazine 88(5), 323–336 (2015)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.820394Z

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This paper cites [Online; accessed 17 July 2024].

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi [Online; accessed 17 July 2024]

Reference 39

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This paper cites Science 365(6456), 885–890 (2019) https://doi.org/10.1126/science.aay2400.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Science 365(6456), 885–890 (2019) https://doi.org/10.1126/science.aay2400

Reference 40

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This paper cites Advances in neural information processing systems 35, 24611–24624 (2022).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Advances in neural information processing systems 35, 24611–24624 (2022)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.777433Z

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This paper cites Advances in neural information processing systems 30 (2017).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Advances in neural information processing systems 30 (2017)

Reference 42

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This paper cites Nature 575(7782), 350–354 (2019).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Nature 575(7782), 350–354 (2019)

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.735007Z

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This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 44

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This paper cites In: International Conference on Learning Representation (2021).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: International Conference on Learning Representation (2021)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.718064Z

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This paper cites In: International Conference on Learning Representation (2021).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: International Conference on Learning Representation (2021)

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.699606Z

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This paper cites Journal of Artificial Intelligence Research 64, 817–859 (2019) https://doi.org/10.1613/jair.1.11418.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Journal of Artificial Intelligence Research 64, 817–859 (2019) https://doi.org/10.1613/jair.1.11418

Reference 47

Resolution: verified exact; doi, observed 2026-08-11T19:51:20.340047Z

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This paper cites Advances in Neural Information Processing Systems 35, 30406–30418 (2022).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Advances in Neural Information Processing Systems 35, 30406–30418 (2022)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.679377Z

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This paper cites Advances in Neural Information Processing Systems 34, 14502–14515 (2021).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Advances in Neural Information Processing Systems 34, 14502–14515 (2021)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.661116Z

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This paper cites Advances in neural information processing systems 32 (2019).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Advances in neural information processing systems 32 (2019)

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.644538Z

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This paper cites 1, 2nd edn.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi 1, 2nd edn

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.616177Z

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.

Observation 7a743da8-91a2-4721-8fbc-698aa3940c49 · outbound

This paper cites 1, 1st edn.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi 1, 1st edn

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.596745Z

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.

Observation 724aa07a-9cfb-428c-845e-174e47c61e43 · outbound

This paper cites cooperative agents.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi cooperative agents

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.580492Z

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.

Observation 3b7823f0-f8bc-4dfd-806a-5530b951e9cb · outbound

This paper cites Nature 518(7540), 529–533 (2015).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Nature 518(7540), 529–533 (2015)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.555485Z

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.

Observation 2875bef9-ab0c-427a-9cd7-d07c8c5bd970 · outbound

This paper cites Machine learning 8(3), 293–321 (1992) https://doi.org/10.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Machine learning 8(3), 293–321 (1992) https://doi.org/10

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.522310Z

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.

Observation c4cfefec-becb-4772-a80a-dc3c66a29747 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Adam: A Method for Stochastic Optimization

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T19:51:20.187189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99a06788-1d38-4cda-a115-95ebfa06a901 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi In: International Conference on Machine Learning, pp

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.498880Z

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.

Observation 83e56e28-3bc8-4ed8-8b93-618dfe01b9c7 · outbound

This paper cites Nature 588(7839), 604–609 (2020).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Nature 588(7839), 604–609 (2020)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.477329Z

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.

Observation c4095638-2396-4e7f-9b25-2b88e84d0587 · outbound

This paper cites IEEE communications surveys & tutorials 21(4), 3133–3174 (2019) https://doi.org/10.1109/COMST.2019.2916583.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi IEEE communications surveys & tutorials 21(4), 3133–3174 (2019) https://doi.org/10.1109/COMST.2019.2916583

Reference 59

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T19:51:20.837463Z

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.

Observation 7ef58708-ef80-4ea2-8453-77e82fe11fe8 · outbound

This paper cites IEEE Communications surveys & tutorials 21(3), 2224–2287 39 (2019) https://doi.org/10.1109/COMST.2019.2904897.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi IEEE Communications surveys & tutorials 21(3), 2224–2287 39 (2019) https://doi.org/10.1109/COMST.2019.2904897

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T19:51:20.214183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 130e3119-fca1-4f28-8372-d66acc4340e9 · outbound

This paper cites The Annals of Statistics 48(4), 1875–1897 (2020).

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi The Annals of Statistics 48(4), 1875–1897 (2020)

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.455121Z

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.

Observation a47b4f2d-894d-4d0e-ad79-07f748f6ee95 · outbound

This paper cites Neurocomputing 190, 82–94 (2016) https://doi.org/10.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Neurocomputing 190, 82–94 (2016) https://doi.org/10

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:51:21.435926Z

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.

Observation 75ea72ef-5637-4b15-8ae2-fe1f460de6eb · outbound

This paper cites Biometrika 34(1-2), 28–35 (1947) https://doi.org/ 10.1093/biomet/34.1-2.28 40.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Biometrika 34(1-2), 28–35 (1947) https://doi.org/ 10.1093/biomet/34.1-2.28 40

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T19:51:20.232220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers