Michael L. Littman joined Brown University's Computer Science Department after ten years (including 3 as chair) at Rutgers University. His research in machine learning examines algorithms for decision making under uncertainty. Littman has earned multiple awards for teaching and his research has been recognized with three best-paper awards on the topics of meta-learning for computer crossword solving, complexity analysis of planning under uncertainty, and algorithms for efficient reinforcement learning. He has served on the editorial boards of the Journal of Machine Learning Research and the Journal of Artificial Intelligence Research. In 2013, he was general chair of the International Conference on Machine Learning (ICML) and program co-chair of the Association for the Advancement of Artificial Intelligence Conference and he served as program co-chair of ICML 2009.
| Loftin, Robert, Peng, Bei, MacGlashan, James, Littman, Michael L., Taylor, Matthew E., Huang, Jeff, Roberts, David L. "Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning." Auton Agent Multi-Agent Syst, vol. 30, no. 1, 2015, pp. 30-59. |
| Littman, Michael L. "Reinforcement learning improves behaviour from evaluative feedback." J. Geophys. Res., vol. 521, no. 7553, 2015, pp. 445-451. |
| Loftin, Robert, Peng, Bei, MacGlashan, James, Littman, Michael L., Taylor, Matthew E., Huang, Jeff, Roberts, David L. "Learning something from nothing: Leveraging implicit human feedback strategies." The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014. |
| Ur, Blase, McManus, Elyse, Pak Yong Ho, Melwyn, Littman, Michael L. "Practical trigger-action programming in the smart home." Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI '14, 2014. |
| Littman, Michael L. "A new way to search game trees." Communications of the ACM, vol. 55, no. 3, 2012, pp. 105. |
| Vlassis, Nikos, Littman, Michael L., Barber, David. "On the Computational Complexity of Stochastic Controller Optimization in POMDPs." ACM Transactions on Computation Theory, vol. 4, no. 4, 2012, pp. 1-8. |
| Clyde, Merlise A., Ghosh, Joyee, Littman, Michael L. "Bayesian Adaptive Sampling for Variable Selection and Model Averaging." Journal of Computational and Graphical Statistics, vol. 20, no. 1, 2011, pp. 80-101. |
| Yaman, Fusun, Walsh, Thomas J., Littman, Michael L., desJardins, Marie. "Democratic approximation of lexicographic preference models." Artificial Intelligence, vol. 175, no. 7-8, 2011, pp. 1290-1307. |
| Russell, Brian, Littman, Michael L., Trappe, Wade. "Integrating machine learning in ad hoc routing: A wireless adaptive routing protocol." Int. J. Commun. Syst., vol. 24, no. 7, 2011, pp. 950-966. |
| Whiteson, Shimon, Littman, Michael L. "Introduction to the special issue on empirical evaluations in reinforcement learning." Mach Learn, vol. 84, no. 1-2, 2011, pp. 1-6. |
| Yuan, Changhe, Lim, Heejin, Littman, Michael L. "Most Relevant Explanation: computational complexity and approximation methods." Annals of Mathematics and Artificial Intelligence, vol. 61, no. 3, 2011, pp. 159-183. |
| Littman, Michael L., Reeves, Daniel. "Puzzle." SIGecom Exch., vol. 10, no. 1, 2011, pp. 39-39. |
| Ash, Jordan, Babes, Monica, Cohen, Gal, Jalal, Sameen, Lichtenberg, Sam, Littman, Michael, Marivate, Vukosi, Quiza, Phillip, Ur, Blase, Zhang, Emily. "Scratchable Devices: User-Friendly Programming for Household Appliances." Automata, Languages and Programming, 2011, pp. 137-146. |
| Goschin, Sergiu, Littman, Michael L., Ackley, David H. "The effects of selection on noisy fitness optimization." Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11, 2011. |
| desJardins, Marie, Littman, Michael. "Broadening student enthusiasm for computer science with a great insights course." Proceedings of the 41st ACM technical symposium on Computer science education - SIGCSE '10, 2010. |
| Nouri, Ali, Littman, Michael L. "Dimension reduction and its application to model-based exploration in continuous spaces." Mach Learn, vol. 81, no. 1, 2010, pp. 85-98. |
| Li, Lihong, Littman, Michael L., Walsh, Thomas J., Strehl, Alexander L. "Knows what it knows: a framework for self-aware learning." Mach Learn, vol. 82, no. 3, 2010, pp. 399-443. |
| Li, Lihong, Littman, Michael L. "Reducing reinforcement learning to KWIK online regression." Annals of Mathematics and Artificial Intelligence, vol. 58, no. 3-4, 2010, pp. 217-237. |
| Littman, Michael L. "A tutorial on partially observable Markov decision processes." Journal of Mathematical Psychology, vol. 53, no. 3, 2009, pp. 119-125. |
| Parr, Ronald, Li, Lihong, Taylor, Gavin, Painter-Wakefield, Christopher, Littman, Michael L. "An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning." Proceedings of the 25th international conference on Machine learning - ICML '08, 2008. |
| Strehl, Alexander L., Littman, Michael L. "An analysis of model-based Interval Estimation for Markov Decision Processes." Journal of Computer and System Sciences, vol. 74, no. 8, 2008, pp. 1309-1331. |
| Diuk, Carlos, Cohen, Andre, Littman, Michael L. "An object-oriented representation for efficient reinforcement learning." Proceedings of the 25th international conference on Machine learning - ICML '08, 2008. |
| Littman, Michael. "Autonomous Model Learning for Reinforcement Learning." 2008 Fifth International Conference on Quantitative Evaluation of Systems, 2008. |
| Buja, Andreas, Swayne, Deborah F, Littman, Michael L, Dean, Nathaniel, Hofmann, Heike, Chen, Lisha. "Data Visualization With Multidimensional Scaling." Journal of Computational and Graphical Statistics, vol. 17, no. 2, 2008, pp. 444-472. |
| Yaman, Fusun, Walsh, Thomas J., Littman, Michael L., desJardins, Marie. "Democratic approximation of lexicographic preference models." Proceedings of the 25th international conference on Machine learning - ICML '08, 2008. |
| Li, Lihong, Littman, Michael L., Walsh, Thomas J. "Knows what it knows." Proceedings of the 25th international conference on Machine learning - ICML '08, 2008. |
| Walsh, Thomas J., Nouri, Ali, Li, Lihong, Littman, Michael L. "Learning and planning in environments with delayed feedback." Auton Agent Multi-Agent Syst, vol. 18, no. 1, 2008, pp. 83-105. |
| Roberts, David L., Isbell, Charles L., Littman, Michael L. "Optimization problems involving collections of dependent objects." Annals of Operations Research, vol. 163, no. 1, 2008, pp. 255-270. |
| Zinkevich, Martin, Greenwald, Amy, Littman, Michael L. "A hierarchy of prescriptive goals for multiagent learning." Artificial Intelligence, vol. 171, no. 7, 2007, pp. 440-447. |
| Parr, Ronald, Painter-Wakefield, Christopher, Li, Lihong, Littman, Michael. "Analyzing feature generation for value-function approximation." Proceedings of the 24th international conference on Machine learning - ICML '07, 2007. |
| Walsh, Thomas J., Nouri, Ali, Li, Lihong, Littman, Michael L. "Planning and Learning in Environments with Delayed Feedback." Automata, Languages and Programming, 2007, pp. 442-453. |
| Diuk, Carlos, Strehl, Alexander L., Littman, Michael L. "A hierarchical approach to efficient reinforcement learning in deterministic domains." Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems - AAMAS '06, 2006. |
| Strehl, Alexander L., Mesterharm, Chris, Littman, Michael L., Hirsh, Haym. "Experience-efficient learning in associative bandit problems." Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006. |
| Strehl, Alexander L., Li, Lihong, Wiewiora, Eric, Langford, John, Littman, Michael L. "PAC model-free reinforcement learning." Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006. |
| Littman, Michael L., Stone, Peter. "A polynomial-time Nash equilibrium algorithm for repeated games." Decision Support Systems, vol. 39, no. 1, 2005, pp. 55-66. |
| Strehl, Alexander L., Littman, Michael L. "A theoretical analysis of Model-Based Interval Estimation." Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005. |
| Chadha, R., Poylisher, A., Deb, B., Littman, M., Sabata, B. "Adaptive Dynamic Server Placement in Manets." MILCOM 2005 - 2005 IEEE Military Communications Conference, 2005. |
| Turney, Peter D., Littman, Michael L. "Corpus-based Learning of Analogies and Semantic Relations." Mach Learn, vol. 60, no. 1-3, 2005, pp. 251-278. |
| Strehl, A.L., Littman, M.L. "An empirical evaluation of interval estimation for Markov decision processes." 16th IEEE International Conference on Tools with Artificial Intelligence, 2004. |
| James, M.R., Singh, S., Littman, M.L. "Planning with predictive state representations." 2004 International Conference on Machine Learning and Applications, 2004. Proceedings., 2004. |
| Littman, M.L., Ravi, N., Fenson, E., Howard, R. "Reinforcement learning for autonomic network repair." International Conference on Autonomic Computing, 2004. Proceedings., 2004. |
| Year | Degree | Institution |
|---|---|---|
| 1996 | PhD | Brown University |
| 1988 | BS | Yale University |
| 1988 | MS | Yale University |
| Name | Title |
|---|---|
| Cushman, Fiery | Assistant Professor of Cognitive, Linguistic and Psychological Sciences |
| Frank, Michael | Edgar L. Marston Professor of Psychology, Professor of Brain Science |
| Greenwald, Amy | Associate Professor of Computer Science |
| Jenkins, Odest | Associate Professor of Computer Science, Associate Professor of Engineering |
| Malle, Bertram | Professor of Cognitive and Psychological Sciences |
| Sloman, Steven | Professor of Cognitive and Psychological Sciences |
Brown University: Professor of Computer Science
Rutgers University: Adjunct Professor
Georgia Tech: Adjunct Professor
Oneacross.com: Co-founder and CEO
Consultant
| CSCI 0080 - A First Byte of Computer Science |
| CSCI 0220 - Introduction to Discrete Structures and Probability |
| CSCI 1420 - Machine Learning |
| CSCI 2951F - Learning and Sequential Decision Making |
| ENGN 2520 - Pattern Recognition and Machine Learning |
