World's Best Scientists 2026 revealed!
Michael L. Littman

Michael L. Littman

D-Index & Metrics

Computer Science

D-Index
91
Citations
54131
World Ranking
559
National Ranking
297

Michael L. Littman publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Michael L. Littman sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 323 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Michael L. Littman D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Michael L. Littman sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 91 D-Index — 96th percentile

96% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2018 - ACM Fellow For contributions to the design and analysis of sequential decision making algorithms in artificial intelligence
  • 2010 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the fields of reinforcement learning, decision making under uncertainty, and statistical language applications.

Overview

Michael L. Littman is affiliated with Brown University in the United States. Their primary research field is Computer Science, with a focus on multiple subfields including Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Cognitive Neuroscience, and Software.

The scientist's main research topics cover a range of areas such as Reinforcement Learning in Robotics, Machine Learning and Algorithms, Formal Methods in Verification, Adversarial Robustness in Machine Learning, Evolutionary Algorithms and Applications, Explainable Artificial Intelligence (XAI), and Machine Learning and Data Classification.

Recent publications by Michael L. Littman include the following papers:

  • Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report, 2022, arXiv (Cornell University)
  • Collusion rings threaten the integrity of computer science research, 2021, Communications of the ACM

Littman has frequently collaborated with several coauthors, including:

  • George Konidaris
  • David Abel
  • Kavosh Asadi
  • Mark K. Ho
  • Shangqun Yu

Publication venues where Michael L. Littman has contributed extensively include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Communications of the ACM
  • Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
  • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

The scientist has authored books published by The MIT Press, including Code to Joy (2023).

Michael L. Littman has received professional recognition through awards such as:

  • ACM Fellow (2018) for contributions to the design and analysis of sequential decision-making algorithms in artificial intelligence
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) (2010) for significant contributions to reinforcement learning, decision making under uncertainty, and statistical language applications

Best Publications

  • Reinforcement learning: a survey

    Leslie Pack Kaelbling;Michael L. Littman;Andrew W. Moore

  • Planning and Acting in Partially Observable Stochastic Domains

    Leslie Pack Kaelbling;Michael L. Littman;Anthony R. Cassandra

  • Markov games as a framework for multi-agent reinforcement learning

    Michael L. Littman

  • Measuring praise and criticism: Inference of semantic orientation from association

    Peter D. Turney;Michael L. Littman

  • Activity recognition from accelerometer data

    Nishkam Ravi;Nikhil Dandekar;Preetham Mysore;Michael L. Littman

  • Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach

    Justin A. Boyan;Michael L. Littman

  • Learning policies for partially observable environments: scaling up

    Michael L. Littman;Anthony R. Cassandra;Leslie Pack Kaelbling

  • Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

    Satinder Singh;Tommi Jaakkola;Michael L. Littman;Csaba Szepesvári

  • Acting Optimally in Partially Observable Stochastic Domains

    Anthony R. Cassandra;Leslie Pack Kaelbling;Michael L. Littman

  • Predictive Representations of State

    Michael L. Littman;Richard S Sutton

  • Friend-or-Foe Q-learning in General-Sum Games

    Michael L. Littman

  • Graphical models for game theory

    Michael J. Kearns;Michael L. Littman;Satinder P. Singh

  • Algorithms for Sequential Decision Making

    Michael Lederman Littman

  • On the complexity of solving Markov decision problems

    Michael L. Littman;Thomas L. Dean;Leslie Pack Kaelbling

  • PAC model-free reinforcement learning

    Alexander L. Strehl;Lihong Li;Eric Wiewiora;John Langford

  • Computerized cross-language document retrieval using latent semantic indexing

    Thomas K. Landauer;Michael L. Littman

  • Value-function reinforcement learning in Markov games

    Michael L. Littman

  • Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes

    Anthony Cassandra;Michael L. Littman;Nevin L. Zhang

  • Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

    Peter D. Turney;Michael L. Littman

  • Data Visualization With Multidimensional Scaling

    Andreas Buja;Deborah F Swayne;Michael L Littman;Nathaniel Dean

  • An analysis of model-based Interval Estimation for Markov Decision Processes

    Alexander L. Strehl;Michael L. Littman

Frequent Co-Authors

Lihong Li
Lihong Li Amazon (United States)
Tom Walsh
Tom Walsh University of Washington
Peter Stone
Peter Stone The University of Texas at Austin
Satinder Singh
Satinder Singh DeepMind (United Kingdom)
George Konidaris
George Konidaris Brown University
Thomas K. Landauer
Thomas K. Landauer University of Colorado Boulder
Peter D. Turney
Peter D. Turney Ronin Institute
Fiery Cushman
Fiery Cushman Harvard University
Thomas L. Griffiths
Thomas L. Griffiths Princeton University

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