Kagan Tumer

4.3k total citations · 1 hit paper
126 papers, 2.5k citations indexed

About

Kagan Tumer is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Networks and Communications. According to data from OpenAlex, Kagan Tumer has authored 126 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Artificial Intelligence, 29 papers in Management Science and Operations Research and 21 papers in Computer Networks and Communications. Recurrent topics in Kagan Tumer's work include Reinforcement Learning in Robotics (51 papers), Auction Theory and Applications (16 papers) and Data Stream Mining Techniques (15 papers). Kagan Tumer is often cited by papers focused on Reinforcement Learning in Robotics (51 papers), Auction Theory and Applications (16 papers) and Data Stream Mining Techniques (15 papers). Kagan Tumer collaborates with scholars based in United States, United Kingdom and Switzerland. Kagan Tumer's co-authors include Joydeep Ghosh, Adrian Agogino, David H. Wolpert, Nikunj C. Oza, Jen Jen Chung, Damjan Miklić, Roland Siegwart, Lorenzo Sabattini, Kevin Wheeler and Rebecca Richards‐Kortum and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, Europhysics Letters (EPL) and Pattern Recognition Letters.

In The Last Decade

Kagan Tumer

113 papers receiving 2.4k citations

Hit Papers

Proceedings of the 18th International Conference on Auton... 2019 2026 2021 2023 2019 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kagan Tumer United States 25 1.4k 379 376 365 285 126 2.5k
Brahim Chaib-draa Canada 27 1.3k 0.9× 289 0.8× 351 0.9× 418 1.1× 577 2.0× 147 2.7k
Ronald Parr United States 26 1.7k 1.2× 447 1.2× 453 1.2× 362 1.0× 440 1.5× 56 2.7k
Alberto Colorni Italy 16 1.1k 0.8× 196 0.5× 473 1.3× 295 0.8× 315 1.1× 41 3.1k
Daniele Magazzeni United Kingdom 24 1.4k 1.0× 144 0.4× 323 0.9× 679 1.9× 336 1.2× 86 2.6k
José M. Molina Spain 27 1.1k 0.8× 150 0.4× 372 1.0× 590 1.6× 255 0.9× 288 2.7k
Mahardhika Pratama Singapore 30 2.0k 1.4× 256 0.7× 199 0.5× 352 1.0× 581 2.0× 157 3.0k
Luis Magdalena Spain 25 2.6k 1.8× 477 1.3× 215 0.6× 377 1.0× 476 1.7× 93 3.6k
Shimon Whiteson Netherlands 27 2.3k 1.6× 423 1.1× 612 1.6× 466 1.3× 462 1.6× 136 3.5k
Ramón López de Mántaras Spain 24 1.3k 0.9× 220 0.6× 190 0.5× 447 1.2× 143 0.5× 110 2.1k
Riashat Islam Canada 7 910 0.6× 112 0.3× 395 1.1× 293 0.8× 411 1.4× 9 2.1k

Countries citing papers authored by Kagan Tumer

Since Specialization
Citations

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

Fields of papers citing papers by Kagan Tumer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kagan Tumer

This figure shows the co-authorship network connecting the top 25 collaborators of Kagan Tumer. A scholar is included among the top collaborators of Kagan Tumer 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 Kagan Tumer. Kagan Tumer 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.
Loftin, Robert, et al.. (2023). Novelty Seeking Multiagent Evolutionary Reinforcement Learning. Proceedings of the Genetic and Evolutionary Computation Conference. 402–410. 2 indexed citations
2.
Tumer, Kagan, et al.. (2021). Dynamic Skill Selection for Learning Joint Actions. Autonomous Agents and Multi-Agent Systems. 1637–1639. 1 indexed citations
3.
Tumer, Kagan, et al.. (2019). Memory based Multiagent One Shot Learning. Adaptive Agents and Multi-Agents Systems. 2054–2056.
4.
Chung, Jen Jen, et al.. (2018). When Less is More: Reducing Agent Noise with Probabilistically Learning Agents. Adaptive Agents and Multi-Agents Systems. 1900–1902. 3 indexed citations
5.
Tumer, Kagan, et al.. (2018). A Memory-based Multiagent Framework for Adaptive Decision Making. Adaptive Agents and Multi-Agents Systems. 1977–1979. 2 indexed citations
6.
Tumer, Kagan, et al.. (2018). Evolutionary Reinforcement Learning. arXiv (Cornell University). 5 indexed citations
7.
Chung, Jen Jen, et al.. (2016). Local Approximation of Difference Evaluation Functions. Adaptive Agents and Multi-Agents Systems. 521–529. 12 indexed citations
8.
Curran, William J., et al.. (2015). Approximating Difference Evaluations with Local Information. Adaptive Agents and Multi-Agents Systems. 1659–1660. 4 indexed citations
9.
Tumer, Kagan, et al.. (2015). Counterfactual Exploration for Improving Multiagent Learning. Adaptive Agents and Multi-Agents Systems. 171–179. 5 indexed citations
10.
Tumer, Kagan, et al.. (2014). Announced strategy types in multiagent RL for conflict-avoidance in the national airspace. NASA STI Repository (National Aeronautics and Space Administration).
11.
Tumer, Kagan, et al.. (2014). Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms. NASA STI Repository (National Aeronautics and Space Administration). 3 indexed citations
12.
Curran, William J., et al.. (2014). Approximating difference evaluations with local knowledge. Adaptive Agents and Multi-Agents Systems. 1577–1578. 1 indexed citations
13.
Agogino, Adrian, et al.. (2013). CLEAN rewards for improving multiagent coordination in the presence of exploration. Adaptive Agents and Multi-Agents Systems. 1113–1114. 2 indexed citations
14.
Agogino, Adrian, et al.. (2013). Exploiting structure and utilizing agent-centric rewards to promote coordination in large multiagent systems. Adaptive Agents and Multi-Agents Systems. 1181–1182. 2 indexed citations
15.
Tumer, Kagan & Adrian Agogino. (2008). Adaptive management of air traffic flow: a multiagent coordination approach. National Conference on Artificial Intelligence. 1581–1584. 5 indexed citations
16.
Agogino, Adrian & Kagan Tumer. (2006). QUICR-learning for multi-agent coordination. National Conference on Artificial Intelligence. 1438–1443. 11 indexed citations
17.
Agogino, Adrian & Kagan Tumer. (2004). Unifying Temporal and Structural Credit Assignment Problems. Adaptive Agents and Multi-Agents Systems. 3. 980–987. 44 indexed citations
18.
Tumer, Kagan & Peter Stone. (2002). Collaborative learning agents : papers from the 2002 AAAI Symposium, March 25-27, Stanford, California. 1 indexed citations
19.
Wolpert, David H., Kevin Wheeler, & Kagan Tumer. (1998). Distributed Control with Collective Intelligence. Neural Information Processing Systems. 2 indexed citations
20.
Tumer, Kagan, Nirmala Ramanujam, Rebecca Richards‐Kortum, & Joydeep Ghosh. (1996). Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks. Neural Information Processing Systems. 981–987. 5 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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