Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
DeepStack: Expert-level artificial intelligence in heads-up no-limit poker
2017409 citationsNeil Burch, Nolan Bard et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Kevin Waugh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kevin Waugh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Waugh more than expected).
This network shows the impact of papers produced by Kevin Waugh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kevin Waugh. The network helps show where Kevin Waugh may publish in the future.
Co-authorship network of co-authors of Kevin Waugh
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Waugh.
A scholar is included among the top collaborators of Kevin Waugh based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Kevin Waugh. Kevin Waugh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Waugh, Kevin & J. Andrew Bagnell. (2014). A Unified View of Large-Scale Zero-Sum Equilibrium Computation.. arXiv (Cornell University).3 indexed citations
3.
Waugh, Kevin. (2013). A Fast and Optimal Hand Isomorphism Algorithm. National Conference on Artificial Intelligence. 77. b148–b148.5 indexed citations
Bowers, David, et al.. (2011). Exploring the Essence of an Object-Relational Impedance Mismatch - A novel technique based on Equivalence in the context of a Framework. Open Research Online (The Open University). 65–70.2 indexed citations
7.
Ganzfried, Sam, Tüomas Sandholm, & Kevin Waugh. (2011). Strategy purification. Adaptive Agents and Multi-Agents Systems. 1111–1112.2 indexed citations
Logie, Robert H., Jon G. Hall, & Kevin Waugh. (2010). Investigating agent influence and nested other-agent behaviour. Open Research Online (The Open University). 3. 106–120.1 indexed citations
10.
Waugh, Kevin, Nolan Bard, & Michael Bowling. (2009). Strategy Grafting in Extensive Games. Neural Information Processing Systems. 22. 2026–2034.12 indexed citations
11.
Waugh, Kevin, et al.. (2009). A Practical Use of Imperfect Recall.37 indexed citations
12.
Bowling, Michael, Nolan Bard, Darse Billings, et al.. (2009). A demonstration of the Polaris poker system. Adaptive Agents and Multi-Agents Systems. 1391–1392.4 indexed citations
Thomas, Peter, Kevin Waugh, & Neil Smith. (2008). A revision tool for teaching and learning sequence diagrams. Open Research Online (The Open University). 2008(1). 5454–5460.4 indexed citations
15.
Szafron, Duane, et al.. (2007). A Demonstration of SQUEGE: A CRPG Sub-Quest Generator. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 3(1). 110–111.3 indexed citations
16.
Thomas, Peter, Kevin Waugh, & Neil Smith. (2007). Tools for supporting the teaching and learning of data modelling. Open Research Online (The Open University). 2007(1). 4014–4023.4 indexed citations
17.
Logie, Robert H., Jon G. Hall, & Kevin Waugh. (2006). Reactive food gathering. Open Research Online (The Open University).1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.