Michael Johanson

2.5k total citations · 1 hit paper
19 papers, 1.2k citations indexed

About

Michael Johanson is a scholar working on Artificial Intelligence, Economics and Econometrics and Management Science and Operations Research. According to data from OpenAlex, Michael Johanson has authored 19 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 10 papers in Economics and Econometrics and 6 papers in Management Science and Operations Research. Recurrent topics in Michael Johanson's work include Artificial Intelligence in Games (18 papers), Sports Analytics and Performance (10 papers) and Reinforcement Learning in Robotics (7 papers). Michael Johanson is often cited by papers focused on Artificial Intelligence in Games (18 papers), Sports Analytics and Performance (10 papers) and Reinforcement Learning in Robotics (7 papers). Michael Johanson collaborates with scholars based in Canada, Czechia and United Kingdom. Michael Johanson's co-authors include Michael Bowling, Neil Burch, Martin Zinkevich, Kevin Waugh, Nolan Bard, Trevor Davis, Matej Moravčík, Martin Schmid, Viliam Lisý and Dustin Morrill and has published in prestigious journals such as Science, Communications of the ACM and Artificial Intelligence.

In The Last Decade

Michael Johanson

19 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

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

2017 409 citations Matej Moravčík, Martin Schmid et al. Science profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Johanson Canada	14	915	328	300	207	190	19	1.2k
Kevin Waugh United Kingdom	15	700 0.8×	186 0.6×	192 0.6×	141 0.7×	93 0.5×	56	1.1k
Noam Brown United States	9	495 0.5×	109 0.3×	170 0.6×	105 0.5×	69 0.4×	19	692
Nolan Bard Canada	9	443 0.5×	126 0.4×	93 0.3×	92 0.4×	84 0.4×	17	654
Viliam Lisý Czechia	11	415 0.5×	100 0.3×	113 0.4×	83 0.4×	46 0.2×	35	725
Feng-hsiung Hsu China	12	598 0.7×	171 0.5×	30 0.1×	177 0.9×	30 0.2×	25	1.1k
Trevor Davis United States	5	293 0.3×	75 0.2×	59 0.2×	94 0.5×	41 0.2×	8	480
Dustin Morrill Canada	4	285 0.3×	71 0.2×	69 0.2×	58 0.3×	37 0.2×	7	437
Piotr J. Gmytrasiewicz United States	18	749 0.8×	55 0.2×	301 1.0×	123 0.6×	11 0.1×	67	1.1k
Matej Moravčík Czechia	3	282 0.3×	72 0.2×	59 0.2×	62 0.3×	40 0.2×	7	424
Raz Lin Israel	16	597 0.7×	84 0.3×	278 0.9×	148 0.7×	9 0.0×	29	848

Countries citing papers authored by Michael Johanson

Since Specialization

Citations

This map shows the geographic impact of Michael Johanson'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 Johanson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Johanson more than expected).

Fields of papers citing papers by Michael Johanson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Johanson. 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 Johanson. The network helps show where Michael Johanson may publish in the future.

Co-authorship network of co-authors of Michael Johanson

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Johanson. A scholar is included among the top collaborators of Michael Johanson 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 Johanson. Michael Johanson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Johanson, Michael, Nolan Bard, Neil Burch, & Michael Bowling. (2021). Finding Optimal Abstract Strategies in Extensive-Form Games. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 1371–1379. 4 indexed citations

Bachrach, Yoram, Richard Everett, Edward Hughes, et al.. (2020). Negotiating team formation using deep reinforcement learning. Artificial Intelligence. 288. 103356–103356. 16 indexed citations

Bowling, Michael, et al.. (2017). Heads-up limit hold'em poker is solved. Communications of the ACM. 60(11). 81–88. 12 indexed citations

Moravčík, Matej, Martin Schmid, Neil Burch, et al.. (2017). DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science. 356(6337). 508–513. 409 indexed citations breakdown →

Burch, Neil, et al.. (2015). Solving heads-up limit Texas Hold'em. International Conference on Artificial Intelligence. 645–652. 28 indexed citations

Bowling, Michael, et al.. (2015). Heads-up limit hold’em poker is solved. Science. 347(6218). 145–149. 170 indexed citations

Bard, Nolan, Michael Johanson, & Michael Bowling. (2014). Asymmetric abstractions for adversarial settings. Adaptive Agents and Multi-Agents Systems. 501–508. 3 indexed citations

Burch, Neil, Michael Johanson, & Michael Bowling. (2014). Solving Imperfect Information Games Using Decomposition. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1). 25 indexed citations

Johanson, Michael, Neil Burch, Richard Valenzano, & Michael Bowling. (2013). Evaluating state-space abstractions in extensive-form games. Adaptive Agents and Multi-Agents Systems. 271–278. 34 indexed citations

10.

Bard, Nolan, Michael Johanson, Neil Burch, & Michael Bowling. (2013). Online implicit agent modelling. Adaptive Agents and Multi-Agents Systems. 255–262. 24 indexed citations

11.

Johanson, Michael, Nolan Bard, Marc Lanctot, Richard G. Gibson, & Michael Bowling. (2012). Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization. Adaptive Agents and Multi-Agents Systems. 837–846. 23 indexed citations

12.

Johanson, Michael, Kevin Waugh, Michael Bowling, & Martin Zinkevich. (2011). Accelerating best response calculation in large extensive games. International Joint Conference on Artificial Intelligence. 258–265. 42 indexed citations

13.

Johanson, Michael & Michael Bowling. (2009). Data Biased Robust Counter Strategies. International Conference on Artificial Intelligence and Statistics. 264–271. 30 indexed citations

14.

Waugh, Kevin, et al.. (2009). A Practical Use of Imperfect Recall. 37 indexed citations

15.

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

16.

Bowling, Michael, Michael Johanson, Neil Burch, & Duane Szafron. (2008). Strategy evaluation in extensive games with importance sampling. 72–79. 17 indexed citations

17.

Johanson, Michael, Michael Bowling, & Martin Zinkevich. (2007). Computing Robust Counter-Strategies. University of Alberta Library. 20. 721–728. 42 indexed citations

18.

Bowling, Michael, et al.. (2007). Regret Minimization in Games with Incomplete Information. University of Alberta Library. 20. 1729–1736. 260 indexed citations

19.

Hayward, Ryan, et al.. (2005). Solving 7×7 Hex with domination, fill-in, and virtual connections. Theoretical Computer Science. 349(2). 123–139. 5 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.

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