Daniel Whitehouse

3.3k total citations · 1 hit paper
15 papers, 1.9k citations indexed

About

Daniel Whitehouse is a scholar working on Artificial Intelligence, Sociology and Political Science and Economics and Econometrics. According to data from OpenAlex, Daniel Whitehouse has authored 15 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Sociology and Political Science and 5 papers in Economics and Econometrics. Recurrent topics in Daniel Whitehouse's work include Artificial Intelligence in Games (14 papers), Digital Games and Media (7 papers) and Reinforcement Learning in Robotics (7 papers). Daniel Whitehouse is often cited by papers focused on Artificial Intelligence in Games (14 papers), Digital Games and Media (7 papers) and Reinforcement Learning in Robotics (7 papers). Daniel Whitehouse collaborates with scholars based in United Kingdom. Daniel Whitehouse's 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 and has published in prestigious journals such as Artificial Intelligence, IEEE Transactions on Computational Intelligence and AI in Games and Array.

In The Last Decade

Daniel Whitehouse

15 papers receiving 1.8k citations

Hit Papers

A Survey of Monte Carlo Tree Search Methods 2012 2026 2016 2021 2012 500 1000 1.5k
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.

  1. A Survey of Monte Carlo Tree Search Methods
    2012 1.6k citations Cameron Browne, Edward J. Powley et al. IEEE Transactions on Computational Intelligence and AI in Games profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Whitehouse United Kingdom 10 1.3k 374 350 246 164 15 1.9k
Edward J. Powley United Kingdom 12 1.3k 1.1× 391 1.0× 398 1.1× 267 1.1× 165 1.0× 29 2.0k
Philipp Rohlfshagen United Kingdom 13 1.3k 1.1× 386 1.0× 335 1.0× 208 0.8× 194 1.2× 24 2.1k
Cameron Browne Netherlands 8 1.7k 1.3× 592 1.6× 650 1.9× 224 0.9× 173 1.1× 56 2.4k
Diego Pérez-Liébana United Kingdom 18 2.0k 1.6× 559 1.5× 704 2.0× 305 1.2× 186 1.1× 100 2.8k
Sylvain Gelly France 17 1.2k 1.0× 591 1.6× 235 0.7× 311 1.3× 86 0.5× 46 1.7k
Santiago Ontañón United States 21 1.5k 1.2× 523 1.4× 385 1.1× 79 0.3× 125 0.8× 139 2.1k
Carlos Cotta Spain 17 978 0.8× 170 0.5× 115 0.3× 78 0.3× 211 1.3× 112 1.8k
Aske Plaat Netherlands 18 627 0.5× 163 0.4× 117 0.3× 88 0.4× 494 3.0× 82 1.5k
Itsuki Noda Japan 17 741 0.6× 509 1.4× 130 0.4× 51 0.2× 245 1.5× 85 1.6k
Changjie Fan China 21 593 0.5× 696 1.9× 93 0.3× 51 0.2× 84 0.5× 98 1.5k

Countries citing papers authored by Daniel Whitehouse

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

15 of 15 papers shown
1.
Rebollo‐Neira, Laura & Daniel Whitehouse. (2019). Sparse representation of 3D images for piecewise dimensionality reduction with high quality reconstruction. Array. 1-2. 100001–100001. 3 indexed citations
2.
Powley, Edward J., Peter Cowling, & Daniel Whitehouse. (2017). Memory Bounded Monte Carlo Tree Search. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 13(1). 94–100. 7 indexed citations
3.
Cowling, Peter, Daniel Whitehouse, & Edward J. Powley. (2015). Emergent bluffing and inference with Monte Carlo Tree Search. 114–121. 4 indexed citations
4.
Powley, Edward J., Peter Cowling, & Daniel Whitehouse. (2014). Information capture and reuse strategies in Monte Carlo Tree Search, with applications to games of hidden information. Artificial Intelligence. 217. 92–116. 14 indexed citations
5.
Cowling, Peter, et al.. (2014). Parallelization of Information Set Monte Carlo Tree Search. 4630. 2290–2297. 5 indexed citations
6.
Cowling, Peter, et al.. (2014). Player Preference and Style in a Leading Mobile Card Game. IEEE Transactions on Computational Intelligence and AI in Games. 7(3). 233–242. 10 indexed citations
7.
Pérez-Liébana, Diego, Edward J. Powley, Daniel Whitehouse, et al.. (2014). The 2013 Multi-objective Physical Travelling Salesman Problem Competition. 2314–2321. 2 indexed citations
8.
Whitehouse, Daniel, et al.. (2013). Integrating Monte Carlo Tree Search with Knowledge-Based Methods to Create Engaging Play in a Commercial Mobile Game. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 9(1). 100–105. 13 indexed citations
9.
Pérez-Liébana, Diego, Edward J. Powley, Daniel Whitehouse, et al.. (2013). Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions. IEEE Transactions on Computational Intelligence and AI in Games. 6(1). 31–45. 36 indexed citations
10.
Powley, Edward J., Daniel Whitehouse, & Peter Cowling. (2013). Monte Carlo Tree Search with macro-actions and heuristic route planning for the Multiobjective Physical Travelling Salesman Problem. 1–8. 13 indexed citations
11.
Powley, Edward J., Daniel Whitehouse, & Peter Cowling. (2013). Bandits all the way down: UCB1 as a simulation policy in Monte Carlo Tree Search. 1–8. 13 indexed citations
12.
Cowling, Peter, Edward J. Powley, & Daniel Whitehouse. (2012). Information Set Monte Carlo Tree Search. IEEE Transactions on Computational Intelligence and AI in Games. 4(2). 120–143. 93 indexed citations
13.
Browne, Cameron, Edward J. Powley, Daniel Whitehouse, et al.. (2012). A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games. 4(1). 1–43. 1605 indexed citations breakdown →
14.
Powley, Edward J., Daniel Whitehouse, & Peter Cowling. (2012). Monte Carlo Tree Search with macro-actions and heuristic route planning for the Physical Travelling Salesman Problem. 1. 234–241. 22 indexed citations
15.
Whitehouse, Daniel, Edward J. Powley, & Peter Cowling. (2011). Determinization and information set Monte Carlo Tree Search for the card game Dou Di Zhu. 87–94. 31 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Edward J. Powley Breakdown of academic impact, for papers by Philipp Rohlfshagen Breakdown of academic impact, for papers by Cameron Browne Breakdown of academic impact, for papers by Diego Pérez-Liébana Breakdown of academic impact, for papers by Sylvain Gelly Breakdown of academic impact, for papers by Santiago Ontañón Breakdown of academic impact, for papers by Carlos Cotta Breakdown of academic impact, for papers by Aske Plaat Breakdown of academic impact, for papers by Itsuki Noda Breakdown of academic impact, for papers by Changjie Fan
Rankless by CCL
2026