Aske Plaat

3.3k citations

82 papers · 1.5k indexed · 2 hit papers · h-index 18

Impact in

Hardware and Architecture top 2%
- Parallel Computing and Optimization Techniques
Computer Networks and Communications top 2%
- Distributed and Parallel Computing Systems
- Advanced Data Storage Technologies
- Distributed systems and fault tolerance
- Interconnection Networks and Systems

Papers in

Hardware and Architecture 11
- Parallel Computing and Optimization Techniques 11
Artificial Intelligence 41
- Artificial Intelligence in Games 21
- Reinforcement Learning in Robotics 11
- Domain Adaptation and Few-Shot Learning 6

Co-authors: Henri E. Bal Jan N. van Rijn Rutger F. H. Hofman Thomas M. Moerland Joost Broekens Catholijn M. Jonker Thilo Kielmann Jonathan Schaeffer
Journals: Machine Learning (4 papers)Astronomy and Computing (2 papers)Artificial Intelligence Review (2 papers)ACM SIGPLAN Notices (2 papers)Computer Physics Communications (1 paper)
Partner nations: Netherlands Canada Czechia

In The Last Decade

Aske Plaat

73 papers receiving 1.4k citations

Hit Papers

align trajectories log scale

Model-based Reinforcement Learning: A Survey 2023 · 220 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2023 Research Repository (Delft University of Technology)
2021 Data Archiving and Networked Services (DANS)

Peers

Countries citing papers authored by Aske Plaat

Since Specialization

Citations

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

Fields of papers citing papers by Aske Plaat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Aske Plaat, 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 Aske Plaat Line = papers co-authored together Aske Plaat links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work
1	Multi-Step Reasoning with Large Language Models, a Survey ACM Computing Surveys ·Aske Plaat, Егор Ельницкий, Suzan Verberne, Joost Broekens, Bas van Stein, Thomas Bäck	2025	0
2	A Hybrid Intelligence Method for Argument Mining Journal of Artificial Intelligence Research ·Michiel van der Meer, Αργυρώ Μπίστη, Catholijn M. Jonker, Aske Plaat, Piek Vossen, Pradeep K. Murukannaiah	2024	0
3	Are LSTMs good few-shot learners? Machine Learning ·Joseph Gangemi, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn	2023	9
4	Model-based Reinforcement Learning: A Survey Research Repository (Delft University of Technology) ·Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker Hit paper breakdown →	2023	220
5	Understanding transfer learning and gradient-based meta-learning techniques Machine Learning ·Joseph Gangemi, Aske Plaat, Jan N. van Rijn	2023	6
6	Subspace Adaptation Prior for Few-Shot Learning Machine Learning ·Joseph Gangemi, Aske Plaat, Jan N. van Rijn	2023	1
7	Deep multiagent reinforcement learning: challenges and directions Artificial Intelligence Review ·Егор Ельницкий, Thomas Bäck, Anna V. Kononova, Aske Plaat	2022	91
8	A Unifying Framework for Reinforcement Learning and Planning Frontiers in Artificial Intelligence ·Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker	2022	9
9	Analysis of Hyper-Parameters for AlphaZero-Like Deep Reinforcement Learning International Journal of Information Technology & Decision Making ·笠原希仁, Michael Emmerich, Mike Preuß, Aske Plaat	2022	2
10	A survey of deep meta-learning Data Archiving and Networked Services (DANS) ·Joseph Gangemi, Jan N. van Rijn, Aske Plaat Hit paper breakdown →	2021	255
11	Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning arXiv (Cornell University) ·Mohammad Reza Rezazadeh, Mike Preuß, Aske Plaat	2021	6
12	Model-Based Deep Reinforcement Learning for High-Dimensional Problems, a Survey. arXiv (Cornell University) ·Aske Plaat, Walter A. Kosters, Mike Preuß	2020	10
13	Pipeline Collector: Gathering performance data for distributed astronomical pipelines Astronomy and Computing ·A. P. Mechev, Aske Plaat, J. B. R. Oonk, H. T. Intema, H. J. A. Röttgering	2018	3
14	Comparing Voluntary and Mandatory Gameplay International Journal of Serious Games ·Viviana Soledad Alarcón-González, 英夫飯尾, H.J. van den Herik, Aske Plaat	2016	6
15	An Empirical Study into Social Success Factors for Agile Software Development Radboud Repository (Radboud University) ·Jidan Sandika Hidayat, Joost Visser, Aske Plaat, Jonas F Weber	2015	14
16	PsyOps : personality assessment through gaming behavior Data Archiving and Networked Services (DANS) ·Emilie Anduran, Pieter Spronck, Aske Plaat, H.J. van den Herik, Jan Broersen	2013	33
17	Improving multivariate Horner schemes with Monte Carlo tree search Computer Physics Communications ·Bette Chapman, Aske Plaat, J.A.M. Vermaseren, H.J. van den Herik	2013	10
18	Exploiting graph properties of game trees National Conference on Artificial Intelligence ·Aske Plaat, Jonathan Schaeffer, Wim Pijls, Arie de Bruin	1996	22
19	Best-first fixed-depth minimax algorithms Artificial Intelligence ·Aske Plaat, Jonathan Schaeffer, Wim Pijls, Arie de Bruin	1996	62
20	Best-first fixed-depth game-tree search in practice Data Archiving and Networked Services (DANS) ·Aske Plaat, Jonathan Schaeffer, Wim Pijls, Arie de Bruin	1995	9

About Aske Plaat

Aske Plaat is a scholar working on Hardware and Architecture, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Developmental and Educational Psychology and Computer Vision and Pattern Recognition, having authored 82 papers that have together received 1.5k indexed citations. Recurring topics across this work include Artificial Intelligence in Games (21 papers), Reinforcement Learning in Robotics (11 papers), Parallel Computing and Optimization Techniques (11 papers), Digital Games and Media (10 papers), Educational Games and Gamification (9 papers), Distributed and Parallel Computing Systems (8 papers), Sports Analytics and Performance (7 papers) and Domain Adaptation and Few-Shot Learning (6 papers). The work is most often cited by research in Hardware and Architecture (334 citations), Computer Networks and Communications (494 citations), Artificial Intelligence (627 citations), Computer Vision and Pattern Recognition (163 citations) and Information Systems (174 citations). Aske Plaat has collaborated with scholars based in Netherlands, Canada and Czechia. Frequent co-authors include Henri E. Bal, Jan N. van Rijn, Rutger F. H. Hofman, Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker, Thilo Kielmann, Jonathan Schaeffer, R.A.F. Bhoedjang and H.J. van den Herik. Their work appears in journals such as Machine Learning, Astronomy and Computing, Artificial Intelligence Review, ACM SIGPLAN Notices and Computer Physics Communications.

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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