The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022

Ryan W. Gardner, Gino Perrotta, Anvay Shah, Shivaram Kalyanakrishnan, Kevin A. Wang, Gregory Clark, Timo Bertram, Johannes Fürnkranz, Martin Müller, Brady P. Garrison, Prithviraj Dasgupta, Saeid Rezaei
Proceedings of the NeurIPS 2022 Competitions Track, PMLR 220:119-132, 2022.

Abstract

Reconnaissance Blind Chess is a game that plays like regular chess but rather than continuously observing the entire board, each player can only momentarily and privately observe selected board regions. It has imperfect information and little common knowledge. The Johns Hopkins University Applied Physics Laboratory (the game’s creator) and several partners organized the third NeurIPS machine Reconnaissance Blind Chess competition in 2022 to bring people together to attempt to tackle research challenges presented by the game. 18 bots played each other in 9,180 games (60 matches per bot pair) over 4 days. The top bot exceeded the performance of all of last year’s bots yet a practical, sound (unexploitable) algorithm remains unknown.

Cite this Paper

BibTeX
@InProceedings{pmlr-v220-gardner23a, title = {The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022}, author = {Gardner, Ryan W. and Perrotta, Gino and Shah, Anvay and Kalyanakrishnan, Shivaram and Wang, Kevin A. and Clark, Gregory and Bertram, Timo and F\"{u}rnkranz, Johannes and M\"{u}ller, Martin and Garrison, Brady P. and Dasgupta, Prithviraj and Rezaei, Saeid}, booktitle = {Proceedings of the NeurIPS 2022 Competitions Track}, pages = {119--132}, year = {2022}, editor = {Ciccone, Marco and Stolovitzky, Gustavo and Albrecht, Jacob}, volume = {220}, series = {Proceedings of Machine Learning Research}, month = {28 Nov--09 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v220/gardner23a/gardner23a.pdf}, url = {https://proceedings.mlr.press/v220/gardner23a.html}, abstract = {Reconnaissance Blind Chess is a game that plays like regular chess but rather than continuously observing the entire board, each player can only momentarily and privately observe selected board regions. It has imperfect information and little common knowledge. The Johns Hopkins University Applied Physics Laboratory (the game’s creator) and several partners organized the third NeurIPS machine Reconnaissance Blind Chess competition in 2022 to bring people together to attempt to tackle research challenges presented by the game. 18 bots played each other in 9,180 games (60 matches per bot pair) over 4 days. The top bot exceeded the performance of all of last year’s bots yet a practical, sound (unexploitable) algorithm remains unknown.} }
Endnote
%0 Conference Paper %T The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022 %A Ryan W. Gardner %A Gino Perrotta %A Anvay Shah %A Shivaram Kalyanakrishnan %A Kevin A. Wang %A Gregory Clark %A Timo Bertram %A Johannes Fürnkranz %A Martin Müller %A Brady P. Garrison %A Prithviraj Dasgupta %A Saeid Rezaei %B Proceedings of the NeurIPS 2022 Competitions Track %C Proceedings of Machine Learning Research %D 2022 %E Marco Ciccone %E Gustavo Stolovitzky %E Jacob Albrecht %F pmlr-v220-gardner23a %I PMLR %P 119--132 %U https://proceedings.mlr.press/v220/gardner23a.html %V 220 %X Reconnaissance Blind Chess is a game that plays like regular chess but rather than continuously observing the entire board, each player can only momentarily and privately observe selected board regions. It has imperfect information and little common knowledge. The Johns Hopkins University Applied Physics Laboratory (the game’s creator) and several partners organized the third NeurIPS machine Reconnaissance Blind Chess competition in 2022 to bring people together to attempt to tackle research challenges presented by the game. 18 bots played each other in 9,180 games (60 matches per bot pair) over 4 days. The top bot exceeded the performance of all of last year’s bots yet a practical, sound (unexploitable) algorithm remains unknown.
APA
Gardner, R.W., Perrotta, G., Shah, A., Kalyanakrishnan, S., Wang, K.A., Clark, G., Bertram, T., Fürnkranz, J., Müller, M., Garrison, B.P., Dasgupta, P. & Rezaei, S.. (2022). The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022. Proceedings of the NeurIPS 2022 Competitions Track, in Proceedings of Machine Learning Research 220:119-132 Available from https://proceedings.mlr.press/v220/gardner23a.html.

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