Nolan Bard
Impact in
- Artificial Intelligence top 5%
- Artificial Intelligence in Games
- Reinforcement Learning in Robotics
- Explainable Artificial Intelligence (XAI)
- Health Informatics top 10%
Papers in
-
- Artificial Intelligence in Games 15
- Reinforcement Learning in Robotics 6
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- Sports Analytics and Performance 7
- Co-authors
- Michael Bowling (14 shared papers)Neil Burch (7 shared papers)Michael Johanson (6 shared papers)Kevin Waugh (4 shared papers)Martin Schmid (2 shared papers)Matej Moravčík (2 shared papers)Viliam Lisý (1 shared paper)Trevor Davis (1 shared paper)
- Journals
- Computers in Human Behavior (1 paper)Science Advances (1 paper)AI Magazine (1 paper)Artificial Intelligence (1 paper)Science (1 paper)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Nolan Bard
17 papers receiving 713 citations
Nolan Bard's Hit Papers
Peers
Comparison fields: 5 of 105
- Artificial Intelligence 523
- Health Informatics 15
- Management Science and Operations Research 121
- Safety Research 45
- Economics and Econometrics 144
Countries citing papers authored by Nolan Bard
This map shows the geographic impact of Nolan Bard'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 Nolan Bard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nolan Bard more than expected).
Fields of papers citing papers by Nolan Bard
This network shows the impact of papers produced by Nolan Bard. 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 Nolan Bard. The network helps show where Nolan Bard may publish in the future.
Co-authors
The 25 scholars most cited alongside Nolan Bard, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | DeepStack: Expert-level artificial intelligence in heads-up no-limit poker Hit paper breakdown → | 2017 | 472 |
| 2 | 2019 | 123 | |
| 3 | 2013 | 27 | |
| 4 | 2012 | 26 | |
| 5 | 2013 | 23 | |
| 6 | Particle filtering for dynamic agent modelling in simplified poker | 2007 | 15 |
| 7 | Optimal unbiased estimators for evaluating agent performance | 2006 | 14 |
| 8 | Strategy Grafting in Extensive Games | 2009 | 12 |
| 9 | 2013 | 11 | |
| 10 | 2015 | 8 | |
| 11 | 2009 | 7 | |
| 12 | 2022 | 6 | |
| 13 | 2021 | 6 | |
| 14 | The Trellis Security Infrastructure: A Layered Approach to Overlay Metacomputers . | 2004 | 5 |
| 15 | 2023 | 5 | |
| 16 | 2014 | 3 | |
| 17 | 2016 | 2 |
About Nolan Bard
Nolan Bard is a scholar working on Artificial Intelligence, Economics and Econometrics, Clinical Psychology, Sociology and Political Science and Management Science and Operations Research, having authored 17 papers that have together received 765 indexed citations. Recurring topics across this work include Artificial Intelligence in Games (15 papers), Sports Analytics and Performance (7 papers), Gambling Behavior and Treatments (6 papers), Reinforcement Learning in Robotics (6 papers), Advanced Bandit Algorithms Research (3 papers), Digital Games and Media (3 papers), Game Theory and Applications (2 papers) and Evolutionary Game Theory and Cooperation (2 papers). The work is most often cited by research in Artificial Intelligence (523 citations), Health Informatics (15 citations), Management Science and Operations Research (121 citations), Safety Research (45 citations) and Economics and Econometrics (144 citations). Nolan Bard has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Michael Bowling, Neil Burch, Michael Johanson, Kevin Waugh, Martin Schmid, Matej Moravčík, Viliam Lisý, Trevor Davis, Dustin Morrill and Marc Lanctot. Their work appears in journals such as Computers in Human Behavior, Science Advances, AI Magazine, Artificial Intelligence and Science.
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.