Michael Barlow

1.3k total citations
75 papers, 823 citations indexed

About

Michael Barlow is a scholar working on Artificial Intelligence, Sociology and Political Science and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michael Barlow has authored 75 papers receiving a total of 823 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 14 papers in Sociology and Political Science and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michael Barlow's work include Evolutionary Algorithms and Applications (11 papers), Reinforcement Learning in Robotics (9 papers) and Evolutionary Game Theory and Cooperation (9 papers). Michael Barlow is often cited by papers focused on Evolutionary Algorithms and Applications (11 papers), Reinforcement Learning in Robotics (9 papers) and Evolutionary Game Theory and Cooperation (9 papers). Michael Barlow collaborates with scholars based in Australia, Japan and United States. Michael Barlow's co-authors include Hussein A. Abbass, Kathryn Kasmarik, Sameer Alam, Lam Thu Bui, Raul Fernandez Rojas, Sreenatha G. Anavatti, Matthew Garratt, Essam Debie, Justin Fidock and Axel Bender and has published in prestigious journals such as IEEE Access, ACM Computing Surveys and Modern Language Journal.

In The Last Decade

Michael Barlow

74 papers receiving 787 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Barlow Australia 16 230 149 126 93 82 75 823
Kathryn Kasmarik Australia 14 222 1.0× 129 0.9× 91 0.7× 57 0.6× 62 0.8× 53 643
Mobyen Uddin Ahmed Sweden 17 360 1.6× 158 1.1× 158 1.3× 42 0.5× 23 0.3× 64 1.1k
Dylan A. Shell United States 18 368 1.6× 80 0.5× 288 2.3× 92 1.0× 78 1.0× 86 1.1k
Ming Hou Canada 17 294 1.3× 47 0.3× 168 1.3× 60 0.6× 39 0.5× 101 866
Tetsuo Sawaragi Japan 15 296 1.3× 55 0.4× 210 1.7× 43 0.5× 32 0.4× 172 931
Peter Funk Sweden 16 507 2.2× 83 0.6× 49 0.4× 50 0.5× 39 0.5× 87 1.0k
Shahina Begum Sweden 19 493 2.1× 221 1.5× 188 1.5× 39 0.4× 23 0.3× 89 1.3k
Marı́a Teresa López Spain 20 260 1.1× 237 1.6× 96 0.8× 62 0.7× 25 0.3× 83 1.2k
Richard Alan Peters United States 17 315 1.4× 91 0.6× 160 1.3× 198 2.1× 62 0.8× 61 1.1k
Andrea Bonarini Italy 22 612 2.7× 172 1.2× 149 1.2× 150 1.6× 90 1.1× 140 1.5k

Countries citing papers authored by Michael Barlow

Since Specialization
Citations

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

Fields of papers citing papers by Michael Barlow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Barlow

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Barlow. A scholar is included among the top collaborators of Michael Barlow 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 Michael Barlow. Michael Barlow is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kasmarik, Kathryn, et al.. (2024). Competence Awareness for Humans and Machines: A Survey and Future Research Directions from Psychology. ACM Computing Surveys. 57(1). 1–26.
2.
Barlow, Michael, et al.. (2023). Reinforcement Learning Agents Playing Ticket to Ride–A Complex Imperfect Information Board Game With Delayed Rewards. IEEE Access. 11. 60737–60757. 3 indexed citations
3.
Kasmarik, Kathryn, et al.. (2023). Using Abstraction Graphs to Promote Exploration in Curiosity-Inspired Intrinsic Motivation. 505–513. 1 indexed citations
4.
Barlow, Michael, et al.. (2021). Task Allocation in Multi-Agent Systems with Grammar-Based Evolution. UNSWorks (University of New South Wales, Sydney, Australia). 175–182. 2 indexed citations
5.
Barlow, Michael, et al.. (2021). A novel trust architecture integrating differentiated trust and response strategies for a team of agents. International Journal of Intelligent Systems. 36(12). 7017–7052. 1 indexed citations
6.
Rojas, Raul Fernandez, Essam Debie, Justin Fidock, et al.. (2020). Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments. Frontiers in Neuroscience. 14. 40–40. 58 indexed citations
7.
Barlow, Michael, et al.. (2020). Impact of Using Active Locomotion Interfaces for Gameplay: A Study on Exertion Levels and Presence. IEEE Transactions on Games. 12(4). 406–415. 1 indexed citations
8.
Debie, Essam, Raul Fernandez Rojas, Justin Fidock, et al.. (2019). Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review. IEEE Transactions on Cybernetics. 51(3). 1542–1555. 124 indexed citations
9.
Barlow, Michael, et al.. (2019). Prosthetic Rehabilitation Training in Virtual Reality. 1–8. 22 indexed citations
10.
Wang, Min, et al.. (2018). Convolution Neural Networks for Person Identification and Verification Using Steady State Visual Evoked Potential. UNSWorks (University of New South Wales, Sydney, Australia). 1062–1069. 32 indexed citations
11.
Kasmarik, Kathryn, et al.. (2018). Toward Computational Motivation for Multi-Agent Systems and Swarms. Frontiers in Robotics and AI. 5. 134–134. 3 indexed citations
12.
Barlow, Michael, et al.. (2018). Automatic synthesis of swarm behavioural rules from their atomic components. Proceedings of the Genetic and Evolutionary Computation Conference. 133–140. 5 indexed citations
13.
Barlow, Michael, et al.. (2018). Wearable sensors for recognizing individuals undertaking daily activities. 64–67. 14 indexed citations
14.
15.
Abbass, Hussein A., Eleni Petraki, Kathryn Merrick, John D. Harvey, & Michael Barlow. (2015). Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges. Cognitive Computation. 8(3). 385–408. 38 indexed citations
16.
Shah, Zubair, Abdun Naser Mahmood, & Michael Barlow. (2015). Computing discounted multidimensional hierarchical aggregates using modified Misra Gries algorithm. Performance Evaluation. 91. 170–186. 3 indexed citations
17.
Bui, Lam Thu, et al.. (2011). A grid-based heuristic for two-dimensional packing problems. 2329–2336. 3 indexed citations
18.
Bui, Lam Thu, Michael Barlow, & Hussein A. Abbass. (2009). A MULTI-OBJECTIVE RISK-BASED FRAMEWORK FOR MISSION CAPABILITY PLANNING. New Mathematics and Natural Computation. 5(2). 459–485. 26 indexed citations
19.
Abbass, Hussein A., et al.. (2008). Characterizing Game Dynamics in Two-Player Strategy Games Using Network Motifs. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 38(3). 682–690. 17 indexed citations
20.
Abbass, Hussein A., et al.. (2005). Chapter 25 Evolving Capability Requirements in WISDOM-II. 335–347. 1 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.

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