Malcolm Ryan

1.1k citations
53 papers · 769 · h-index 15

Impact in

Papers in

Malcolm Ryan

51 papers receiving 691 citations

Peers

Malcolm Ryan
Comparison fields: 5 of 92
  • Computer Vision and Pattern Recognition 244
  • Safety Research 61
  • Artificial Intelligence 243
  • Developmental and Educational Psychology 86
  • Computer Science Applications 38
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Citations per field
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Citations per year

Countries citing papers authored by Malcolm Ryan

Since Specialization
Citations

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

Fields of papers citing papers by Malcolm Ryan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008154
2 200675
3 202046
4
Graph decomposition for efficient multi-robot path planning
200742
5
The Training of Teachers and Trainers: Innovative Practices, Skills and Competencies in the use of eLearning
200442
6 201642
7 201035
8
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies
200233
9
RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning
199829
10 201725
11 201424
12 198819
13 201718
14
Multi-robot path-planning with subgraphs
200615
15 201715
16 202013
17 201710
18
Learning to Fly: An Application of Hierarchical Reinforcement Learning
20009
19 20009
20 20199

About Malcolm Ryan

Malcolm Ryan is a scholar working on Artificial Intelligence, Sociology and Political Science, Cognitive Neuroscience, Computer Vision and Pattern Recognition and Education, having authored 53 papers that have together received 769 indexed citations. Recurring topics across this work include Digital Games and Media (9 papers), Artificial Intelligence in Games (7 papers), Psychology of Moral and Emotional Judgment (7 papers), Educational Games and Gamification (5 papers), Robotic Path Planning Algorithms (5 papers), Ethics in Business and Education (5 papers), AI-based Problem Solving and Planning (5 papers) and Reinforcement Learning in Robotics (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (244 citations), Safety Research (61 citations), Artificial Intelligence (243 citations), Developmental and Educational Psychology (86 citations) and Computer Science Applications (38 citations). Malcolm Ryan has collaborated with scholars based in Australia, United Kingdom and Canada. Frequent co-authors include Paul Formosa, Jill Jameson, Stacy W. Gray, Mei Ying Boon, Mark D. Reid, Stephen J. Dain, John S. Douglas, Sieu K. Khuu, Catherine Suttle and Barbara M Junghans. Their work appears in journals such as Games and Culture, Ethics and Information Technology, Research in Learning Technology, Behavior Research Methods and Child Care Health and Development.

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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