The Game of Bridge: A Challenge for ILP
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Legras et al. (2018) show that Aleph and TILDE can outperform an SVM learner in the game of Bridge.
“Legras et al. (2018) show that Aleph and TILDE can outperform an SVM learner in the game of Bridge.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Legras et al. (2018)
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Metropolis, N., & Ulam, S. (1949) · Journal of the American Statistical Association · doi:10.1080/01621459.1949.10483310 · Cited by 6,474 · see citing works
Probabilistic Inductive Logic Programming
Raedt, L. D., & Kersting, K. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-78652-8_1 · Cited by 386 · see citing works
Discovery of frequent DATALOG patterns
Dehaspe, L., & Toivonen, H. T. T. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009863704807 · Cited by 315 · see citing works
Statistical predicate invention
Kok, S., & Domingos, P. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273551 · Cited by 270 · see citing works
GIB: Imperfect Information in a Computationally Challenging Game
Ginsberg, M. L. (2001) · Journal of Artificial Intelligence Research · doi:10.1613/jair.820 · Cited by 158 · see citing works
Scaling Up Inductive Logic Programming by Learning from Interpretations
Blockeel, H., Raedt, L. D., Jacobs, N., & Demoen, B. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009867806624 · Cited by 102 · see citing works
RelNN: A Deep Neural Model for Relational Learning
Kazemi, S. M., & Poole, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12111 · Cited by 41 · see citing works
Contract Bridge Bidding by Learning.
Ho, C., & Lin, H.-T. (2015) · Cited by 14 · see citing works
Boosting a Bridge Artificial Intelligence
Ventos, V., Costel, Y., Teytaud, O., & Thépaut, S. (2017) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2017.00193 · Cited by 13 · see citing works
Self-citationCombining Knowledge and Search to Solve Single-Suit Bridge
Frank, I., Basin, D., & Bundy, A. (2000) · Cited by 10 · see citing works
Le bridge, nouveau défi de l’intelligence artificielle ?
Ventos, V., & Teytaud, O. (2017) · Revue d intelligence artificielle · doi:10.3166/ria.31.249-279 · Cited by 9 · see citing works
Self-citationAutomatic Bridge Bidding Using Deep Reinforcement Learning
Yeh, C., & Lin, H.-T. (2016) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-672-9-1362 · Cited by 7 · see citing works
Flagged first, then by citations
1 of the 15 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Inductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
Inductive logic programming at 30
Cropper, A., Dumančić, S., Evans, R., & Muggleton, S. H. (2022) · Machine Learning · doi:10.1007/s10994-021-06089-1 · Cited by 61 · see citing works
Rules, L. O.-R., & A, F. A. L. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48751-4 · Cited by 24 · see citing works
StarAI: Reducing incompleteness in the game of Bridge using PLPPreprint
Li, J., Thépaut, S., & Ventos, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.08193
Self-citationConstruction and Elicitation of a Black Box Model in the Game of Bridge
Ventos, V., Braun, D., Deheeger, C., Desmoulins, J. P., Fantun, J.-B., Legras, S., Rimbaud, A., Rouveirol, C., Soldano, H., & Thépaut, S. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-40403-0_2
Self-citationBeyond broad and narrow: Intermediate level traits in the personality of bridge players
Sauvain, C., Ventos, V., & Sackur, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305985
Self-citation
- Authors
- Legras, S., Rouveirol, C., & Ventos, V.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Rouveirol, Céline
- Ventos, Véronique
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 72-87
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2888355759
- Cited by
- 7
- References
- 15
- Citations by year
- 2024: 3 · 2022: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- bridges, inductive logic programming, bidding decisions, expert validation, supervised learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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