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, Dustin Morrill et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Michael Bowling
Since
Specialization
Citations
This map shows the geographic impact of Michael Bowling'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 Michael Bowling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Bowling more than expected).
This network shows the impact of papers produced by Michael Bowling. 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 Michael Bowling. The network helps show where Michael Bowling may publish in the future.
Co-authorship network of co-authors of Michael Bowling
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Bowling.
A scholar is included among the top collaborators of Michael Bowling 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 Michael Bowling. Michael Bowling is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sturtevant, Nathan, et al.. (2013). Subset selection of search heuristics. International Joint Conference on Artificial Intelligence. 637–643.6 indexed citations
Veness, Joel, Marc Lanctot, & Michael Bowling. (2011). Variance Reduction in Monte-Carlo Tree Search. Neural Information Processing Systems. 24. 1836–1844.8 indexed citations
8.
Lizotte, Daniel J., Michael Bowling, & Susan A. Murphy. (2010). Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis. International Conference on Machine Learning. 695–702.37 indexed citations
9.
Bowling, Michael, et al.. (2009). Probabilistic state translation in extensive games with large action sets. International Joint Conference on Artificial Intelligence. 278–284.24 indexed citations
10.
Johanson, Michael & Michael Bowling. (2009). Data Biased Robust Counter Strategies. International Conference on Artificial Intelligence and Statistics. 264–271.30 indexed citations
11.
Waugh, Kevin, Nolan Bard, & Michael Bowling. (2009). Strategy Grafting in Extensive Games. Neural Information Processing Systems. 22. 2026–2034.12 indexed citations
Bowling, Michael, Alborz Geramifard, & David Wingate. (2008). Sigma point policy iteration. Adaptive Agents and Multi-Agents Systems. 379–386.3 indexed citations
14.
Bard, Nolan & Michael Bowling. (2007). Particle filtering for dynamic agent modelling in simplified poker. National Conference on Artificial Intelligence. 515–521.14 indexed citations
15.
Bowling, Michael, et al.. (2006). Subjective mapping. National Conference on Artificial Intelligence. 1569–1572.3 indexed citations
16.
Sturtevant, Nathan, Martin Zinkevich, & Michael Bowling. (2006). Prob-Max n : playing N-player games with opponent models. National Conference on Artificial Intelligence. 1057–1063.21 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.