2026 Michael L. Littman: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	91	559	543	296	286	323	54131

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

11496
Citations
Planning and Acting in Partially Observable Stochastic Domains

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

5379
Citations
Markov games as a framework for multi-agent reinforcement learning

Michael L. Littman

3512
Citations
Measuring praise and criticism: Inference of semantic orientation from association

Peter D. Turney;Michael L. Littman

2371
Citations
Activity recognition from accelerometer data

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

2112
Citations
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach

Justin A. Boyan;Michael L. Littman

1097
Citations
Learning policies for partially observable environments: scaling up

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

971
Citations
Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

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

947
Citations
Acting Optimally in Partially Observable Stochastic Domains

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

928
Citations
Predictive Representations of State

Michael L. Littman;Richard S Sutton

660
Citations
Friend-or-Foe Q-learning in General-Sum Games

Michael L. Littman

647
Citations
Graphical models for game theory

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

643
Citations
Algorithms for Sequential Decision Making

Michael Lederman Littman

560
Citations
On the complexity of solving Markov decision problems

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

559
Citations
PAC model-free reinforcement learning

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

554
Citations
Computerized cross-language document retrieval using latent semantic indexing

Thomas K. Landauer;Michael L. Littman

544
Citations
Value-function reinforcement learning in Markov games

Michael L. Littman

543
Citations
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes

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

536
Citations
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

Peter D. Turney;Michael L. Littman

493
Citations
Data Visualization With Multidimensional Scaling

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

454
Citations
An analysis of model-based Interval Estimation for Markov Decision Processes

Alexander L. Strehl;Michael L. Littman

431
Citations

Frequent Co-Authors

Lihong Li Amazon (United States)

Leslie Pack Kaelbling MIT

Peter Stone The University of Texas at Austin

Satinder Singh DeepMind (United Kingdom)

George Konidaris Brown University

Thomas K. Landauer University of Colorado Boulder

Peter D. Turney Ronin Institute

Fiery Cushman Harvard University

Thomas L. Griffiths Princeton University

Ronald Parr Duke University

External Links

Wikipedia of Michael L. Littman Personal website of Michael L. Littman Google Scholar page

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