The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Programming backgammon using self-teaching neural nets
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“We also played a match that started from the set of opening positions used in the 2016 TCEC world championship; AlphaZero won convincingly in this match too ( 26 ) (see fig. S4).”
From A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (2018) · cites this paper as [26]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Some Studies in Machine Learning Using the Game of Checkers
Samuel, A. L. (1959) · IBM Journal of Research and Development · doi:10.1147/rd.33.0210 · Cited by 4,443
Temporal difference learning and TD-Gammon
Tesauro, G. (1995) · Communications of the ACM · doi:10.1145/203330.203343 · Cited by 1,618 · cited 1×
“An upgraded version of TD-Gammon, version 2.1, which was trained for 1.5 million games and used 2-ply search, reached the level of a top-flight expert, clearly competitive with the world’s best human players [ 27 , 29].”
MethodInfluentialSelf-citationSome studies in machine learning using the game of checkers
Samuel, A. L. (2000) · IBM Journal of Research and Development · doi:10.1147/rd.441.0206 · Cited by 1,439
Research works printed but not linked: 1
- Robertie, 1992, Carbon versus silicon: Matching wits with TD-Gammon, Inside Backgammon, 2, 14
Cited by
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562
Metadata
- Authors
- Tesauro, G.
- Affiliations
- IBM (United States) (US)
- Corresponding author
- Tesauro, Gerald
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2002-01
- Volume
- 134
- Issue
- 1-2
- Pages
- 181-199
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2014932765
- Cited by
- 153
- References
- 31
- Citations by year
- 2025: 6 · 2024: 5 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 7 · 2019: 2 · 2018: 7 · 2017: 1 · 2016: 3 · 2015: 4 · 2014: 11 · 2013: 7 · 2012: 12
- FWCI
- 7.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 4223 d)
- Locations
- Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- backgammon, reinforcement learning, doubling algorithm
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- CoChat. (2026). Citation check: Programming backgammon using self-teaching neural nets. https://app.cochat.ai/papers/10.1016/s0004-3702(01)00110-2
- The paper
- Tesauro, G. (2002). Programming backgammon using self-teaching neural nets. Artificial Intelligence. https://doi.org/10.1016/s0004-3702(01)00110-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
