Daniel Whitehouse
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Sociology and Political Science top 5%
- Economics and Econometrics top 5%
- Computer Networks and Communications top 10%
- Co-authors
- Peter CowlingEdward J. PowleyDiego Pérez-LiébanaPhilipp RohlfshagenSpyridon SamothrakisSimon M. LucasCameron BrowneSimon Colton
- Topics
- Artificial Intelligence in Games (14 papers)Digital Games and Media (7 papers)Reinforcement Learning in Robotics (7 papers)
- Journals
- Artificial IntelligenceIEEE Transactions on Computational Intelligence and AI in GamesArray
- Partner nations
- United Kingdom
In The Last Decade
Daniel Whitehouse
15 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Artificial Intelligence 1.3k
- Computer Vision and Pattern Recognition 374
- Sociology and Political Science 350
- Economics and Econometrics 246
- Computer Networks and Communications 164
Countries citing papers authored by Daniel Whitehouse
This map shows the geographic impact of Daniel Whitehouse'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 Daniel Whitehouse with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Whitehouse more than expected).
Fields of papers citing papers by Daniel Whitehouse
This network shows the impact of papers produced by Daniel Whitehouse. 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 Daniel Whitehouse. The network helps show where Daniel Whitehouse may publish in the future.
Co-authorship network of co-authors of Daniel Whitehouse
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Whitehouse. A scholar is included among the top collaborators of Daniel Whitehouse 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 Daniel Whitehouse. Daniel Whitehouse is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 7 | |
| 3 | 4 | |
| 4 | 14 | |
| 5 | 5 | |
| 6 | 10 | |
| 7 | 2 | |
| 8 | 13 | |
| 9 | 36 | |
| 10 | 13 | |
| 11 | 13 | |
| 12 | 93 | |
| 13 | A Survey of Monte Carlo Tree Search Methodsbreakdown → | 1605 |
| 14 | 22 | |
| 15 | 31 |
About Daniel Whitehouse
Daniel Whitehouse is a scholar working on Artificial Intelligence, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition, having authored 15 papers that have together received 1.9k indexed citations. Recurring topics across this work include Artificial Intelligence in Games (14 papers), Digital Games and Media (7 papers) and Reinforcement Learning in Robotics (7 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Computer Vision and Pattern Recognition (374 citations) and Automotive Engineering (126 citations). Daniel Whitehouse has collaborated with scholars based in United Kingdom. Frequent co-authors include Peter Cowling, Edward J. Powley, Diego Pérez-Liébana, Philipp Rohlfshagen, Spyridon Samothrakis, Simon M. Lucas, Cameron Browne, Simon Colton, Laura Rebollo‐Neira and Sam Devlin. Their work appears in journals such as Artificial Intelligence, IEEE Transactions on Computational Intelligence and AI in Games and Array.
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.