Charles L. Isbell

3.7k total citations
105 papers, 2.0k citations indexed

About

Charles L. Isbell is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Charles L. Isbell has authored 105 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 20 papers in Computer Vision and Pattern Recognition and 16 papers in Computer Networks and Communications. Recurrent topics in Charles L. Isbell's work include Reinforcement Learning in Robotics (34 papers), Artificial Intelligence in Games (18 papers) and Context-Aware Activity Recognition Systems (11 papers). Charles L. Isbell is often cited by papers focused on Reinforcement Learning in Robotics (34 papers), Artificial Intelligence in Games (18 papers) and Context-Aware Activity Recognition Systems (11 papers). Charles L. Isbell collaborates with scholars based in United States, United Kingdom and Australia. Charles L. Isbell's co-authors include Paul Viola, Jeremy S. De Bonet, Andrea L. Thomaz, Irfan Essa, K.A. Subramanian, David Minnen, Thad Starner, Jonathan Scholz, Michael Mateas and David L. Roberts and has published in prestigious journals such as Proceedings of the IEEE, Communications of the ACM and Artificial Intelligence.

In The Last Decade

Charles L. Isbell

100 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charles L. Isbell United States 22 1.4k 419 295 226 161 105 2.0k
Boyang Li China 22 934 0.7× 529 1.3× 124 0.4× 129 0.6× 111 0.7× 165 2.0k
Charles Rich United States 26 1.7k 1.3× 374 0.9× 379 1.3× 95 0.4× 84 0.5× 112 2.9k
Ashwin Ram United States 23 1.2k 0.9× 288 0.7× 145 0.5× 61 0.3× 111 0.7× 117 1.8k
Simon Colton United Kingdom 20 1.8k 1.3× 728 1.7× 192 0.7× 157 0.7× 505 3.1× 92 2.9k
Dan R. Olsen United States 27 757 0.6× 727 1.7× 212 0.7× 89 0.4× 240 1.5× 92 2.6k
Andrew L. Maas United States 10 2.4k 1.8× 510 1.2× 331 1.1× 410 1.8× 78 0.5× 12 3.2k
Fredrik Heintz Sweden 19 652 0.5× 270 0.6× 127 0.4× 59 0.3× 77 0.5× 97 1.7k
David V. Pynadath United States 22 1.3k 0.9× 171 0.4× 164 0.6× 53 0.2× 220 1.4× 62 1.9k
Lynn Andrea Stein United States 18 1.5k 1.1× 236 0.6× 120 0.4× 133 0.6× 112 0.7× 59 2.3k
Santiago Ontañón United States 21 1.5k 1.1× 523 1.2× 173 0.6× 97 0.4× 385 2.4× 139 2.1k

Countries citing papers authored by Charles L. Isbell

Since Specialization
Citations

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

Fields of papers citing papers by Charles L. Isbell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles L. Isbell. 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 Charles L. Isbell. The network helps show where Charles L. Isbell may publish in the future.

Co-authorship network of co-authors of Charles L. Isbell

This figure shows the co-authorship network connecting the top 25 collaborators of Charles L. Isbell. A scholar is included among the top collaborators of Charles L. Isbell 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 Charles L. Isbell. Charles L. Isbell 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.
Isbell, Charles L., et al.. (2018). Imitating Latent Policies from Observation. International Conference on Machine Learning. 1755–1763. 1 indexed citations
2.
Isbell, Charles L., et al.. (2017). State Aware Imitation Learning. Neural Information Processing Systems. 30. 2911–2920. 8 indexed citations
3.
Subramanian, K.A., Charles L. Isbell, & Andrea L. Thomaz. (2016). Exploration from Demonstration for Interactive Reinforcement Learning. Adaptive Agents and Multi-Agents Systems. 447–456. 34 indexed citations
4.
Harrison, Brent, et al.. (2016). Policy Shaping in Domains with Multiple Optimal Policies: (Extended Abstract). Adaptive Agents and Multi-Agents Systems. 1455–1456. 3 indexed citations
5.
Grover, Ishaan, et al.. (2015). Policy shaping with human teachers. International Conference on Artificial Intelligence. 3366–3372. 36 indexed citations
6.
Scholz, Jonathan, et al.. (2014). A Physics-Based Model Prior for Object-Oriented MDPs. International Conference on Machine Learning. 1089–1097. 22 indexed citations
7.
Cobo, Luis C., Charles L. Isbell, & Andrea L. Thomaz. (2013). Object focused q-learning for autonomous agents. Adaptive Agents and Multi-Agents Systems. 1061–1068. 10 indexed citations
8.
Isbell, Charles L., et al.. (2013). Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs. Neural Information Processing Systems. 26. 100–108. 10 indexed citations
9.
Cobo, Luis C., Charles L. Isbell, & Andrea L. Thomaz. (2012). Automatic task decomposition and state abstraction from demonstration. Adaptive Agents and Multi-Agents Systems. 483–490. 15 indexed citations
10.
Zhou, Peng, et al.. (2010). Using training regimens to teach expanding function approximators. Adaptive Agents and Multi-Agents Systems. 341–348. 6 indexed citations
11.
Roberts, David L., et al.. (2009). Learning to Influence Emotional Responses for Interactive Storytelling. National Conference on Artificial Intelligence. 95–102. 12 indexed citations
12.
Roberts, David L., et al.. (2007). Player Autonomy versus Designer Intent: A Case Study of Interactive Tour Guides. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 3(1). 95–97. 7 indexed citations
13.
Roberts, David L., et al.. (2007). Authorial idioms for target distributions in TTD-MDPs. National Conference on Artificial Intelligence. 852–857. 13 indexed citations
14.
Isbell, Charles L., et al.. (2007). Managing domain knowledge and multiple models with boosting. International Joint Conference on Artificial Intelligence. 1144–1149. 1 indexed citations
15.
Gray, Alexander, et al.. (2007). Ultrafast Monte Carlo for kernel estimators and generalized statistical summations. Neural Information Processing Systems. 673–680. 6 indexed citations
16.
Isbell, Charles L., et al.. (2000). Cobot in LambdaMOO: A Social Statistics Agent. National Conference on Artificial Intelligence. 36–41. 41 indexed citations
17.
Husbands, Parry, Charles L. Isbell, & Alan Edelman. (1999). MITMatlab: A Tool for Interactive Supercomputing.. PPSC. 3 indexed citations
18.
Isbell, Charles L. & Parry Husbands. (1999). The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning. Neural Information Processing Systems. 12. 703–709.
19.
Isbell, Charles L. & Paul Viola. (1998). Restructuring Sparse High Dimensional Data for Effective Retrieval. DSpace@MIT (Massachusetts Institute of Technology). 11. 480–486. 37 indexed citations
20.
Bonet, Jeremy S. De, Charles L. Isbell, & Paul Viola. (1996). MIMIC: Finding Optima by Estimating Probability Densities. Neural Information Processing Systems. 9. 424–430. 299 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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