James MacGlashan

1.4k total citations
30 papers, 486 citations indexed

About

James MacGlashan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, James MacGlashan has authored 30 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Control and Systems Engineering. Recurrent topics in James MacGlashan's work include Reinforcement Learning in Robotics (16 papers), Robot Manipulation and Learning (5 papers) and AI-based Problem Solving and Planning (5 papers). James MacGlashan is often cited by papers focused on Reinforcement Learning in Robotics (16 papers), Robot Manipulation and Learning (5 papers) and AI-based Problem Solving and Planning (5 papers). James MacGlashan collaborates with scholars based in United States, Netherlands and China. James MacGlashan's co-authors include Michael L. Littman, Marie desJardins, Mark K. Ho, Robert Loftin, David L. Roberts, Bei Peng, Matthew E. Taylor, Fiery Cushman, Stefanie Tellex and David Abel and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Cognition and Cognitive Science.

In The Last Decade

James MacGlashan

30 papers receiving 452 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James MacGlashan United States 13 343 108 93 57 53 30 486
Lisa Meeden United States 15 321 0.9× 195 1.8× 159 1.7× 56 1.0× 36 0.7× 44 754
Raquel Ros Spain 11 323 0.9× 133 1.2× 109 1.2× 42 0.7× 203 3.8× 53 532
Kristinn R. Þórisson Iceland 12 374 1.1× 117 1.1× 120 1.3× 72 1.3× 253 4.8× 43 637
Sarath Sreedharan United States 12 480 1.4× 58 0.5× 70 0.8× 19 0.3× 110 2.1× 44 604
Jonathan Sorg United States 8 232 0.7× 34 0.3× 23 0.2× 79 1.4× 27 0.5× 8 357
Ari Weinstein United States 9 158 0.5× 22 0.2× 44 0.5× 113 2.0× 43 0.8× 14 359
Agnese Augello Italy 13 300 0.9× 83 0.8× 112 1.2× 60 1.1× 122 2.3× 80 566
Amy Isard United Kingdom 14 679 2.0× 55 0.5× 83 0.9× 34 0.6× 176 3.3× 48 864
Ricardo Gudwin Brazil 13 270 0.8× 87 0.8× 88 0.9× 90 1.6× 28 0.5× 82 545
Aurélie Clodic France 5 153 0.4× 114 1.1× 63 0.7× 48 0.8× 134 2.5× 13 349

Countries citing papers authored by James MacGlashan

Since Specialization
Citations

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

Fields of papers citing papers by James MacGlashan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James MacGlashan

This figure shows the co-authorship network connecting the top 25 collaborators of James MacGlashan. A scholar is included among the top collaborators of James MacGlashan 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 James MacGlashan. James MacGlashan 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.
Peng, Bei, James MacGlashan, Robert Loftin, et al.. (2018). Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Transactions on Emerging Topics in Computational Intelligence. 2(4). 268–277. 5 indexed citations
2.
Peng, Bei, James MacGlashan, Robert Loftin, et al.. (2017). Curriculum Design for Machine Learners in Sequential Decision Tasks. Adaptive Agents and Multi-Agents Systems. 1682–1684. 3 indexed citations
3.
Kahler, Christopher W., William V. Lechner, James MacGlashan, Tyler B. Wray, & Michael L. Littman. (2017). Initial Progress Toward Development of a Voice-Based Computer-Delivered Motivational Intervention for Heavy Drinking College Students: An Experimental Study. JMIR Mental Health. 4(2). e25–e25. 4 indexed citations
4.
Ho, Mark K., James MacGlashan, Michael L. Littman, & Fiery Cushman. (2017). Social is special: A normative framework for teaching with and learning from evaluative feedback. Cognition. 167. 91–106. 47 indexed citations
5.
Morris, Adam, James MacGlashan, Michael L. Littman, & Fiery Cushman. (2017). Evolution of flexibility and rigidity in retaliatory punishment. Proceedings of the National Academy of Sciences. 114(39). 10396–10401. 10 indexed citations
6.
Peng, Bei, James MacGlashan, Robert Loftin, et al.. (2016). A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. Adaptive Agents and Multi-Agents Systems. 957–965. 16 indexed citations
7.
Ho, Mark K., Michael L. Littman, James MacGlashan, Fiery Cushman, & Joseph L. Austerweil. (2016). Showing versus doing: Teaching by demonstration. Neural Information Processing Systems. 29. 3027–3035. 29 indexed citations
8.
Ho, Mark K., James MacGlashan, Amy Greenwald, et al.. (2016). Feature-based Joint Planning and Norm Learning in Collaborative Games.. Cognitive Science. 5 indexed citations
9.
MacGlashan, James, et al.. (2015). Learning Propositional Functions for Planning and Reinforcement Learning.. National Conference on Artificial Intelligence. 38–45. 3 indexed citations
10.
Tellex, Stefanie, et al.. (2015). Minecraft as an Experimental World for AI in Robotics.. National Conference on Artificial Intelligence. 5–12. 3 indexed citations
11.
Topin, Nicholay, et al.. (2015). Portable option discovery for automated learning transfer in object-oriented Markov decision processes. International Conference on Artificial Intelligence. 3856–3864. 15 indexed citations
12.
MacGlashan, James, Marie desJardins, Michael L. Littman, et al.. (2015). Grounding English Commands to Reward Functions. 38 indexed citations
13.
Loftin, Robert, Bei Peng, James MacGlashan, et al.. (2015). Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Autonomous Agents and Multi-Agent Systems. 30(1). 30–59. 43 indexed citations
14.
desJardins, Marie, et al.. (2014). Discovering Subgoals in Complex Domains.. National Conference on Artificial Intelligence. 1 indexed citations
15.
Barth-Maron, Gabriel, David Abel, James MacGlashan, & Stefanie Tellex. (2014). Affordances as Transferable Knowledge for Planning Agents.. National Conference on Artificial Intelligence. 5 indexed citations
16.
MacGlashan, James, Michael L. Littman, Robert Loftin, et al.. (2014). Training an Agent to Ground Commands with Reward and Punishment. 5 indexed citations
17.
Loftin, Robert, James MacGlashan, Bei Peng, et al.. (2014). A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1). 29 indexed citations
18.
desJardins, Marie & James MacGlashan. (2013). Multi-source option-based policy transfer. 3 indexed citations
19.
desJardins, Marie, James MacGlashan, & Kiri L. Wagstaff. (2010). Confidence-Based Feature Acquisition to Minimize Training and Test Costs. 514–524. 5 indexed citations
20.
desJardins, Marie, et al.. (2007). Interactive visual clustering. 361–364. 27 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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