James MacGlashan
- Artificial Intelligence top 5%
- Control and Systems Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Cognitive Neuroscience
- Social Psychology
- Co-authors
- Michael L. LittmanMarie desJardinsMark K. HoRobert LoftinDavid L. RobertsBei PengMatthew E. TaylorFiery Cushman
- Topics
- Reinforcement Learning in Robotics (16 papers)Robot Manipulation and Learning (5 papers)AI-based Problem Solving and Planning (5 papers)
- Partner nations
- United StatesNetherlandsChina
In The Last Decade
James MacGlashan
30 papers receiving 452 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 343
- Control and Systems Engineering 108
- Computer Vision and Pattern Recognition 93
- Cognitive Neuroscience 57
- Social Psychology 53
Countries citing papers authored by James MacGlashan
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 3 | |
| 3 | 4 | |
| 4 | 47 | |
| 5 | 10 | |
| 6 | 16 | |
| 7 | Showing versus doing: Teaching by demonstration | 29 |
| 8 | Feature-based Joint Planning and Norm Learning in Collaborative Games. | 5 |
| 9 | Learning Propositional Functions for Planning and Reinforcement Learning. | 3 |
| 10 | Minecraft as an Experimental World for AI in Robotics. | 3 |
| 11 | Portable option discovery for automated learning transfer in object-oriented Markov decision processes | 15 |
| 12 | 38 | |
| 13 | 43 | |
| 14 | Discovering Subgoals in Complex Domains. | 1 |
| 15 | Affordances as Transferable Knowledge for Planning Agents. | 5 |
| 16 | Training an Agent to Ground Commands with Reward and Punishment | 5 |
| 17 | 29 | |
| 18 | Multi-source option-based policy transfer | 3 |
| 19 | 5 | |
| 20 | 27 |
About James MacGlashan
James MacGlashan is a scholar working on Artificial Intelligence, Safety Research and Computer Vision and Pattern Recognition, having authored 30 papers that have together received 486 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Robot Manipulation and Learning (5 papers) and AI-based Problem Solving and Planning (5 papers). The work is most often cited by research in Artificial Intelligence (343 citations), Safety Research (53 citations) and Control and Systems Engineering (108 citations). James MacGlashan has collaborated with scholars based in United States, Netherlands and China. Frequent 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. Their work appears in journals such as Proceedings of the National Academy of Sciences, Cognition and Cognitive Science.
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.