Marc Ponsen

745 citations
14 papers · 309 · h-index 8

Impact in

Papers in

Marc Ponsen

12 papers receiving 279 citations

Peers

Marc Ponsen
Comparison fields: 5 of 54
  • Artificial Intelligence 251
  • Developmental and Educational Psychology 60
  • Management Science and Operations Research 35
  • Sociology and Political Science 97
  • Computer Vision and Pattern Recognition 42
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Citations per field
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Citations per year

Countries citing papers authored by Marc Ponsen

Since Specialization
Citations

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

Fields of papers citing papers by Marc Ponsen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside Marc Ponsen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Marc Ponsen Line = papers co-authored together Marc Ponsen links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 2006166
2 200724
3 201423
4
Integrating opponent models with Monte-Carlo tree search in poker
201021
5 200920
6
Automatically acquiring domain knowledge for adaptive game AI using evolutionary learning
200519
7
Bayes-relational learning of opponent models from incomplete information in no-limit poker
200815
8 20099
9
MCRNR: fast computing of restricted Nash responses by means of sampling
20103
10
The dynamics of human behaviour in poker
20083
11
Keeping adaptive game AI interesting
20083
12
Towards relational hierarchical reinforcement learning in computer games
20072
13 20091
14
Bayes-relational opponent modeling in poker
20080

About Marc Ponsen

Marc Ponsen is a scholar working on Artificial Intelligence, Economics and Econometrics, Clinical Psychology, Sociology and Political Science and Management Science and Operations Research, having authored 14 papers that have together received 309 indexed citations. Recurring topics across this work include Artificial Intelligence in Games (10 papers), Evolutionary Algorithms and Applications (5 papers), Sports Analytics and Performance (5 papers), Reinforcement Learning in Robotics (5 papers), Gambling Behavior and Treatments (4 papers), Evolution and Genetic Dynamics (2 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 (251 citations), Developmental and Educational Psychology (60 citations), Management Science and Operations Research (35 citations), Sociology and Political Science (97 citations) and Computer Vision and Pattern Recognition (42 citations). Marc Ponsen has collaborated with scholars based in Netherlands, Belgium and Canada. Frequent co-authors include Pieter Spronck, Eric Postma, I.G. Sprinkhuizen-Kuyper, Héctor Muñoz‐Avila, David W. Aha, Marc Lanctot, Karl Tuyls, Jan Ramon, Guillaume Chaslot and Michael Kaisers. Their work appears in journals such as IEEE Transactions on Computational Intelligence and AI in Games, Machine Learning, Science of Computer Programming, Entertainment Computing and Research Publications (Maastricht University).

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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