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ORCIDMichael L. Littman
Michael Littman 0002
Person information
- affiliation: Brown University, Department of Computer Science, Providence, RI, USA
- affiliation: National Science Foundation (NSF), Information and Intelligent Systems Division, Alexandria, VA, USA
- affiliation (former): Rutgers University, Department of Computer Science, Piscataway, NJ, USA
- affiliation (former): AT&T Labs Research, Florham Park, NJ, USA
- affiliation (former): Duke University, Department of Computer Science, Durham, NC, USA
- affiliation (former): Bellcore, Morristown, NJ, USA
Other persons with the same name
- Michael Littman 0001 — Princeton University, Department of Mechanical & Aerospace Engineering, NJ, USA
2020 – today
- 2025
[c171]Madison Pickering
, Helena Williams, Alison Gan, Weijia He, Hyojae Park, Francisco Piedrahita Velez, Michael L. Littman, Blase Ur:
How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs. CHI 2025: 875:1-875:34- [i78]Yixiang Sun, Haotian Fu, Michael L. Littman, George Dimitri Konidaris:
Knowledge Retention for Continual Model-Based Reinforcement Learning. CoRR abs/2503.04256 (2025) - 2024
- [j56]Cyrus Cousins, Kavosh Asadi, Elita A. Lobo, Michael Littman:
On Welfare-Centric Fair Reinforcement Learning. RLJ 3: 1124-1137 (2024) - [j55]Zhiyuan Zhou, Shreyas Sundara Raman, Henry Sowerby, Michael Littman:
Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior. RLJ 3: 1265-1288 (2024) - [c170]Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris:
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. NeurIPS 2024 - [i77]Max Zuo, Francisco Piedrahita Velez, Xiaochen Li, Michael L. Littman, Stephen H. Bach:
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages. CoRR abs/2407.03321 (2024) - [i76]Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris:
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. CoRR abs/2407.07333 (2024) - 2023
- [j54]Charles Isbell, Michael L. Littman, Peter Norvig:
Software Engineering of Machine Learning Systems. Commun. ACM 66(2): 35-37 (2023) - [j53]Nina Amla, Dilma Da Silva, Michael Littman, Manish Parashar:
NSF on Chien's Grand Challenge for Sustainability. Commun. ACM 66(5): 36-37 (2023) - [j52]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160: 274-296 (2023) - [c169]Cambridge Yang, Michael Littman, Michael Carbin:
Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable. AAAI 2023: 10729-10736 - [c168]Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. AISTATS 2023: 1390-1410 - [c167]Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris:
Meta-learning Parameterized Skills. ICML 2023: 10461-10481 - [i75]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. CoRR abs/2301.07799 (2023) - [i74]Cambridge Yang, Michael Littman, Michael Carbin:
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable. CoRR abs/2303.05518 (2023) - 2022
- [j51]Lefan Zhang, Cyrus Zhou, Michael L. Littman, Blase Ur, Shan Lu:
Helping Users Debug Trigger-Action Programs. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(4): 196:1-196:32 (2022) - [c166]Cambridge Yang, Michael L. Littman, Michael Carbin:
On the (In)Tractability of Reinforcement Learning for LTL Objectives. IJCAI 2022: 3650-3658 - [c165]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward (Extended Abstract). IJCAI 2022: 5254-5258 - [c164]Jamar L. Sullivan Jr., William Brackenbury, Andrew McNut, Kevin Bryson, Kwam Byll, Yuxin Chen, Michael L. Littman, Chenhao Tan, Blase Ur:
Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data. NAACL-HLT 2022: 521-531 - [c163]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola:
Faster Deep Reinforcement Learning with Slower Online Network. NeurIPS 2022 - [c162]Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris:
Model-based Lifelong Reinforcement Learning with Bayesian Exploration. NeurIPS 2022 - [c161]Charles Lovering, Jessica Forde, George Konidaris, Ellie Pavlick, Michael L. Littman:
Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. NeurIPS 2022 - [i73]Bowen He, Sreehari Rammohan, Jessica Zosa Forde, Michael L. Littman:
Does DQN really learn? Exploring adversarial training schemes in Pong. CoRR abs/2203.10614 (2022) - [i72]Henry Sowerby, Zhiyuan Zhou, Michael L. Littman:
Designing Rewards for Fast Learning. CoRR abs/2205.15400 (2022) - [i71]Haotian Fu, Shangqun Yu, Saket Tiwari, George Dimitri Konidaris, Michael Littman:
Meta-Learning Transferable Parameterized Skills. CoRR abs/2206.03597 (2022) - [i70]Haotian Fu, Shangqun Yu, Michael Littman, George Dimitri Konidaris:
Model-based Lifelong Reinforcement Learning with Bayesian Exploration. CoRR abs/2210.11579 (2022) - [i69]Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian K. Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven A. Sloman, Shannon Vallor, Toby Walsh:
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. CoRR abs/2210.15767 (2022) - [i68]Lucas Lehnert, Michael J. Frank, Michael L. Littman:
Reward-Predictive Clustering. CoRR abs/2211.03281 (2022) - [i67]Charles Lovering, Jessica Zosa Forde, George Konidaris, Ellie Pavlick, Michael L. Littman:
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. CoRR abs/2211.14673 (2022) - [i66]Zhiyuan Zhou, Henry Sowerby, Michael L. Littman:
Specifying Behavior Preference with Tiered Reward Functions. CoRR abs/2212.03733 (2022) - 2021
- [j50]Michael L. Littman:
Collusion rings threaten the integrity of computer science research. Commun. ACM 64(6): 43-44 (2021) - [c160]Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman:
Deep Radial-Basis Value Functions for Continuous Control. AAAI 2021: 6696-6704 - [c159]Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman:
Lipschitz Lifelong Reinforcement Learning. AAAI 2021: 8270-8278 - [c158]Mingxuan Li, Michael L. Littman:
Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract). AAAI 2021: 15827-15828 - [c157]Valerie Zhao, Lefan Zhang, Bo Wang, Michael L. Littman, Shan Lu, Blase Ur:
Understanding Trigger-Action Programs Through Novel Visualizations of Program Differences. CHI 2021: 312:1-312:17 - [c156]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. NeurIPS 2021: 7799-7812 - [i65]Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey, Chris De Sa, Michael Littman:
Model Selection's Disparate Impact in Real-World Deep Learning Applications. CoRR abs/2104.00606 (2021) - [i64]Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths:
Control of mental representations in human planning. CoRR abs/2105.06948 (2021) - [i63]Jeff Druce, James Niehaus, Vanessa Moody, David D. Jensen, Michael L. Littman:
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program. CoRR abs/2106.05506 (2021) - [i62]Ishaan Shah, David Halpern, Kavosh Asadi, Michael L. Littman:
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback. CoRR abs/2109.07054 (2021) - [i61]David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong:
Bad-Policy Density: A Measure of Reinforcement Learning Hardness. CoRR abs/2110.03424 (2021) - [i60]Omer Gottesman, Kavosh Asadi, Cameron Allen, Sam Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for RL in Metric Spaces. CoRR abs/2110.12276 (2021) - [i59]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. CoRR abs/2111.00876 (2021) - [i58]Homer Walke, Daniel Ritter, Carl Trimbach, Michael Littman:
Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator. CoRR abs/2111.04147 (2021) - [i57]Cambridge Yang, Michael L. Littman, Michael Carbin:
Reinforcement Learning for General LTL Objectives Is Intractable. CoRR abs/2111.12679 (2021) - [i56]Yiheng Xie, Mingxuan Li, Shangqun Yu, Michael L. Littman:
Learning Generalizable Behavior via Visual Rewrite Rules. CoRR abs/2112.05218 (2021) - [i55]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola:
Deep Q-Network with Proximal Iteration. CoRR abs/2112.05848 (2021) - 2020
- [j49]Lefan Zhang, Weijia He, Olivia Morkved, Valerie Zhao, Michael L. Littman, Shan Lu, Blase Ur:
Trace2TAP: Synthesizing Trigger-Action Programs from Traces of Behavior. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4(3): 104:1-104:26 (2020) - [j48]Lucas Lehnert, Michael L. Littman:
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning. J. Mach. Learn. Res. 21: 196:1-196:53 (2020) - [j47]Lucas Lehnert, Michael L. Littman, Michael J. Frank:
Reward-predictive representations generalize across tasks in reinforcement learning. PLoS Comput. Biol. 16(10) (2020) - [c155]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
People Do Not Just Plan, They Plan to Plan. AAAI 2020: 1300-1307 - [c154]Nishanth Kumar, Michael Fishman, Natasha Danas, Stefanie Tellex, Michael Littman, George Konidaris:
Task Scoping for Efficient Planning in Open Worlds (Student Abstract). AAAI 2020: 13845-13846 - [c153]Zachary Horvitz, Nam Do, Michael L. Littman:
Context-Driven Satirical News Generation. Fig-Lang@ACL 2020: 40-50 - [c152]David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman:
Value Preserving State-Action Abstractions. AISTATS 2020: 1639-1650 - [c151]Guan Wang, Carl Trimbach, Jun Ki Lee, Mark K. Ho, Michael L. Littman:
Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback. HRI 2020: 649-657 - [c150]Sam Saarinen, Evan Cater, Michael L. Littman:
Applying prerequisite structure inference to adaptive testing. LAK 2020: 422-427 - [i54]Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman:
Lipschitz Lifelong Reinforcement Learning. CoRR abs/2001.05411 (2020) - [i53]Kavosh Asadi, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman:
Deep RBF Value Functions for Continuous Control. CoRR abs/2002.01883 (2020) - [i52]Kavosh Asadi, David Abel, Michael Littman:
Learning State Abstractions for Transfer in Continuous Control. CoRR abs/2002.05518 (2020) - [i51]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR abs/2002.05769 (2020) - [i50]Mingxuan Li, Michael L. Littman:
Towards Sample Efficient Agents through Algorithmic Alignment. CoRR abs/2008.03229 (2020) - [i49]Nishanth Kumar, Michael Fishman, Natasha Danas, Michael Littman, Stefanie Tellex, George Konidaris:
Task Scoping: Building Goal-Specific Abstractions for Planning in Complex Domains. CoRR abs/2010.08869 (2020)
2010 – 2019
- 2019
- [j46]John Barr, Michael L. Littman, Marie desJardins:
Decision trees. Inroads 10(3): 56 (2019) - [c149]David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong:
State Abstraction as Compression in Apprenticeship Learning. AAAI 2019: 3134-3142 - [c148]Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum:
Theory of Minds: Understanding Behavior in Groups through Inverse Planning. AAAI 2019: 6163-6170 - [c147]Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris:
Removing the Target Network from Deep Q-Networks with the Mellowmax Operator. AAMAS 2019: 2060-2062 - [c146]Will Brackenbury, Abhimanyu Deora, Jillian Ritchey, Jason Vallee, Weijia He, Guan Wang, Michael L. Littman, Blase Ur:
How Users Interpret Bugs in Trigger-Action Programming. CHI 2019: 552 - [c145]Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris:
Finding Options that Minimize Planning Time. ICML 2019: 3120-3129 - [c144]David Abel, John Winder, Marie desJardins, Michael L. Littman:
The Expected-Length Model of Options. IJCAI 2019: 1951-1958 - [c143]Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris:
DeepMellow: Removing the Need for a Target Network in Deep Q-Learning. IJCAI 2019: 2733-2739 - [c142]Judah Newman, Bowen Wang, Valerie Zhao, Amy Zeng, Michael L. Littman, Blase Ur:
Evidence Humans Provide When Explaining Data-Labeling Decisions. INTERACT (3) 2019: 390-409 - [c141]Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman:
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles. ICSR 2019: 368-377 - [i48]Jacob Beck, Zoe Papakipos, Michael L. Littman:
ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback. CoRR abs/1901.05101 (2019) - [i47]Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum:
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning. CoRR abs/1901.06085 (2019) - [i46]Lucas Lehnert, Michael L. Littman:
Successor Features Support Model-based and Model-free Reinforcement Learning. CoRR abs/1901.11437 (2019) - [i45]Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman:
Deep Reinforcement Learning from Policy-Dependent Human Feedback. CoRR abs/1902.04257 (2019) - [i44]Carl Trimbach, Michael L. Littman:
Teaching with IMPACT. CoRR abs/1903.06209 (2019) - [i43]Kavosh Asadi, Dipendra Misra, Seungchan Kim, Michael L. Littman:
Combating the Compounding-Error Problem with a Multi-step Model. CoRR abs/1905.13320 (2019) - [i42]Robert T. Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts:
Interactive Learning of Environment Dynamics for Sequential Tasks. CoRR abs/1907.08478 (2019) - [i41]Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman:
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles. CoRR abs/1908.08641 (2019) - [i40]John R. Zech, Jessica Zosa Forde, Michael L. Littman:
Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging. CoRR abs/1912.03606 (2019) - 2018
- [j45]Marie desJardins, Michael L. Littman:
Evolutionary huffman encoding. Inroads 9(2): 80 (2018) - [j44]Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Trans. Emerg. Top. Comput. Intell. 2(4): 268-277 (2018) - [c140]David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman:
Bandit-Based Solar Panel Control. AAAI 2018: 7713-7718 - [c139]Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil:
Effectively Learning from Pedagogical Demonstrations. CogSci 2018 - [c138]David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman:
State Abstractions for Lifelong Reinforcement Learning. ICML 2018: 10-19 - [c137]David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman:
Policy and Value Transfer in Lifelong Reinforcement Learning. ICML 2018: 20-29 - [c136]Kavosh Asadi, Dipendra Misra, Michael L. Littman:
Lipschitz Continuity in Model-based Reinforcement Learning. ICML 2018: 264-273 - [i39]Kavosh Asadi, Dipendra Misra, Michael L. Littman:
Lipschitz Continuity in Model-based Reinforcement Learning. CoRR abs/1804.07193 (2018) - [i38]Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman:
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning. CoRR abs/1806.01265 (2018) - [i37]Lucas Lehnert, Michael L. Littman:
Transfer with Model Features in Reinforcement Learning. CoRR abs/1807.01736 (2018) - [i36]Sam Saarinen, Evan Cater, Michael L. Littman:
Personalized Education at Scale. CoRR abs/1809.10025 (2018) - [i35]Yuu Jinnai, David Abel, Michael L. Littman, George Dimitri Konidaris:
Finding Options that Minimize Planning Time. CoRR abs/1810.07311 (2018) - [i34]Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman:
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning. CoRR abs/1811.00128 (2018) - [i33]Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman:
Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR abs/1812.01129 (2018) - [i32]Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael L. Littman, David D. Jensen:
Measuring and Characterizing Generalization in Deep Reinforcement Learning. CoRR abs/1812.02868 (2018) - 2017
- [j43]Doug Fisher, Charles Lee Isbell Jr., Michael L. Littman, Michael Wollowski, Todd W. Neller, Jim Boerkoel:
Ask Me Anything about MOOCs. AI Mag. 38(2): 7-12 (2017) - [c135]Nakul Gopalan, Marie desJardins, Michael L. Littman, James MacGlashan, Shawn Squire, Stefanie Tellex, John Winder, Lawson L. S. Wong:
Planning with Abstract Markov Decision Processes. ICAPS 2017: 480-488 - [c134]Bei Peng, James MacGlashan, Robert T. Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
Curriculum Design for Machine Learners in Sequential Decision Tasks. AAMAS 2017: 1682-1684 - [c133]Mark K. Ho, Michael L. Littman, Joseph L. Austerweil:
Teaching by Intervention: Working Backwards, Undoing Mistakes, or Correcting Mistakes? CogSci 2017 - [c132]Kavosh Asadi, Michael L. Littman:
An Alternative Softmax Operator for Reinforcement Learning. ICML 2017: 243-252 - [c131]James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman:
Interactive Learning from Policy-Dependent Human Feedback. ICML 2017: 2285-2294 - [i31]David Abel, D. Ellis Hershkowitz, Michael L. Littman:
Near Optimal Behavior via Approximate State Abstraction. CoRR abs/1701.04113 (2017) - [i30]James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman:
Interactive Learning from Policy-Dependent Human Feedback. CoRR abs/1701.06049 (2017) - [i29]Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Lee Isbell Jr., Min Wen, James MacGlashan:
Environment-Independent Task Specifications via GLTL. CoRR abs/1704.04341 (2017) - [i28]Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman:
Latent Attention Networks. CoRR abs/1706.00536 (2017) - [i27]Lucas Lehnert, Stefanie Tellex, Michael L. Littman:
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning. CoRR abs/1708.00102 (2017) - [i26]Kavosh Asadi, Cameron Allen, Melrose Roderick, Abdel-rahman Mohamed, George Dimitri Konidaris, Michael L. Littman:
Mean Actor Critic. CoRR abs/1709.00503 (2017) - [i25]Christopher Grimm, Yuhang Song, Michael L. Littman:
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting. CoRR abs/1709.06533 (2017) - [i24]Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman:
Learning Approximate Stochastic Transition Models. CoRR abs/1710.09718 (2017) - 2016
- [j42]Robert T. Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts:
Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Auton. Agents Multi Agent Syst. 30(1): 30-59 (2016) - [c130]David Abel, James MacGlashan, Michael L. Littman:
Reinforcement Learning as a Framework for Ethical Decision Making. AAAI Workshop: AI, Ethics, and Society 2016 - [c129]Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts:
Towards Behavior-Aware Model Learning from Human-Generated Trajectories. AAAI Fall Symposia 2016 - [c128]Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. AAMAS 2016: 957-965 - [c127]Blase Ur, Melwyn Pak Yong Ho, Stephen Brawner, Jiyun Lee, Sarah Mennicken, Noah Picard, Diane Schulze, Michael L. Littman:
Trigger-Action Programming in the Wild: An Analysis of 200, 000 IFTTT Recipes. CHI 2016: 3227-3231 - [c126]Mark K. Ho, James MacGlashan, Amy Greenwald, Michael L. Littman, Elizabeth Hilliard, Carl Trimbach, Stephen Brawner, Josh Tenenbaum, Max Kleiman-Weiner, Joseph L. Austerweil:
Feature-based Joint Planning and Norm Learning in Collaborative Games. CogSci 2016 - [c125]Max Kleiman-Weiner, Mark K. Ho, Joseph L. Austerweil, Michael L. Littman, Josh Tenenbaum:
Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction. CogSci 2016 - [c124]David Abel, D. Ellis Hershkowitz, Michael L. Littman:
Near Optimal Behavior via Approximate State Abstraction. ICML 2016: 2915-2923 - [c123]Pushkar Kolhe, Michael L. Littman, Charles L. Isbell Jr.:
Peer Reviewing Short Answers using Comparative Judgement. L@S 2016: 241-244 - [c122]Mark K. Ho, Michael L. Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil:
Showing versus doing: Teaching by demonstration. NIPS 2016: 3027-3035 - [c121]Stephen Brawner, Michael L. Littman:
Learning User's Preferred Household Organization via Collaborative Filtering Methods. IntRS@RecSys 2016: 48-54 - [i23]Kavosh Asadi, Michael L. Littman:
A New Softmax Operator for Reinforcement Learning. CoRR abs/1612.05628 (2016) - 2015
- [j41]Eric Eaton, Tom Dietterich, Maria L. Gini, Barbara J. Grosz, Charles L. Isbell Jr., Subbarao Kambhampati, Michael L. Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, Michael J. Wooldridge:
Who speaks for AI? AI Matters 2(2): 4-14 (2015) - [j40]Michael L. Littman:
Reinforcement learning improves behaviour from evaluative feedback. Nat. 521(7553): 445-451 (2015) - [c120]Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil:
Teaching with Rewards and Punishments: Reinforcement or Communication? CogSci 2015 - [c119]James MacGlashan, Michael L. Littman:
Between Imitation and Intention Learning. IJCAI 2015: 3692-3698 - [c118]James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang:
Grounding English Commands to Reward Functions. Robotics: Science and Systems 2015 - 2014
- [c117]Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts:
A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. AAAI 2014: 937-943 - [c116]Vukosi Marivate, Jessica Chemali, Emma Brunskill, Michael L. Littman:
Quantifying Uncertainty in Batch Personalized Sequential Decision Making. AAAI Workshop: Modern Artificial Intelligence for Health Analytics 2014 - [c115]Blase Ur, Elyse McManus, Melwyn Pak Yong Ho, Michael L. Littman:
Practical trigger-action programming in the smart home. CHI 2014: 803-812 - [c114]James MacGlashan, Michael L. Littman, Fiery Cushman:
Flexible theft and resolute punishment: Evolutionary dynamics of social behavior among reinforcement-learning agents. CogSci 2014 - [c113]Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts:
Learning something from nothing: Leveraging implicit human feedback strategies. RO-MAN 2014: 607-612 - 2013
- [c112]Marie desJardins, Michael L. Littman:
AAAI-13 Preface. AAAI 2013 - [c111]Vukosi Marivate, Michael L. Littman:
An Ensemble of Linearly Combined Reinforcement-Learning Agents. AAAI (Late-Breaking Developments) 2013 - [c110]Ari Weinstein, Michael L. Littman:
Open-Loop Planning in Large-Scale Stochastic Domains. AAAI 2013: 1436-1442 - [c109]Sergiu Goschin, Ari Weinstein, Michael L. Littman:
The Cross-Entropy Method Optimizes for Quantiles. ICML (3) 2013: 1193-1201 - [c108]Eric Sodomka, Elizabeth Hilliard, Michael L. Littman, Amy Greenwald:
Coco-Q: Learning in Stochastic Games with Side Payments. ICML (3) 2013: 1471-1479 - [e2]Marie desJardins, Michael L. Littman:
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, USA. AAAI Press 2013, ISBN 978-1-57735-615-8 [contents] - [i22]Michael J. Kearns, Michael L. Littman, Satinder Singh:
Graphical Models for Game Theory. CoRR abs/1301.2281 (2013) - [i21]Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang:
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. CoRR abs/1302.1525 (2013) - [i20]Judy Goldsmith, Michael L. Littman, Martin Mundhenk:
The Complexity of Plan Existence and Evaluation in Probabilistic Domains. CoRR abs/1302.1540 (2013) - [i19]Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling:
On the Complexity of Solving Markov Decision Problems. CoRR abs/1302.4971 (2013) - 2012
- [j39]Michael L. Littman:
A new way to search game trees: technical perspective. Commun. ACM 55(3): 105 (2012) - [j38]Nikos Vlassis, Michael L. Littman, David Barber:
On the Computational Complexity of Stochastic Controller Optimization in POMDPs. ACM Trans. Comput. Theory 4(4): 12:1-12:8 (2012) - [j37]Thomas J. Walsh, Michael L. Littman, Alexander Borgida:
Learning web-service task descriptions from traces. Web Intell. Agent Syst. 10(4): 397-421 (2012) - [c107]Zongzhang Zhang, Michael L. Littman, Xiaoping Chen:
Covering Number as a Complexity Measure for POMDP Planning and Learning. AAAI 2012: 1853-1859 - [c106]Michael Wunder, John Robert Yaros, Michael Kaisers, Michael L. Littman:
A framework for modeling population strategies by depth of reasoning. AAMAS 2012: 947-954 - [c105]Ari Weinstein, Michael L. Littman:
Bandit-Based Planning and Learning in Continuous-Action Markov Decision Processes. ICAPS 2012 - [c104]Sergiu Goschin, Ari Weinstein, Michael L. Littman, Erick Chastain:
Planning in Reward-Rich Domains via PAC Bandits. EWRL 2012: 25-42 - [c103]Ari Weinstein, Michael L. Littman, Sergiu Goschin:
Rollout-based Game-tree Search Outprunes Traditional Alpha-beta. EWRL 2012: 155-167 - [c102]Michael L. Littman:
Inducing Partially Observable Markov Decision Processes. ICGI 2012: 145-148 - [i18]John Asmuth, Michael L. Littman:
Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search. CoRR abs/1202.3699 (2012) - [i17]Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman:
Exploring compact reinforcement-learning representations with linear regression. CoRR abs/1205.2606 (2012) - [i16]John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate:
A Bayesian Sampling Approach to Exploration in Reinforcement Learning. CoRR abs/1205.2664 (2012) - [i15]Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy:
CORL: A Continuous-state Offset-dynamics Reinforcement Learner. CoRR abs/1206.3231 (2012) - [i14]Enrique Munoz de Cote, Michael L. Littman:
A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games. CoRR abs/1206.3277 (2012) - [i13]Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich:
An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees. CoRR abs/1206.6855 (2012) - [i12]Alexander L. Strehl, Lihong Li, Michael L. Littman:
Incremental Model-based Learners With Formal Learning-Time Guarantees. CoRR abs/1206.6870 (2012) - 2011
- [j36]Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins:
Democratic approximation of lexicographic preference models. Artif. Intell. 175(7-8): 1290-1307 (2011) - [j35]Changhe Yuan, Heejin Lim, Michael L. Littman:
Most Relevant Explanation: computational complexity and approximation methods. Ann. Math. Artif. Intell. 61(3): 159-183 (2011) - [j34]Brian Russell, Michael L. Littman, Wade Trappe:
Integrating machine learning in ad hoc routing: A wireless adaptive routing protocol. Int. J. Commun. Syst. 24(7): 950-966 (2011) - [j33]Lihong Li, Michael L. Littman, Thomas J. Walsh, Alexander L. Strehl:
Knows what it knows: a framework for self-aware learning. Mach. Learn. 82(3): 399-443 (2011) - [j32]Shimon Whiteson, Michael L. Littman:
Introduction to the special issue on empirical evaluations in reinforcement learning. Mach. Learn. 84(1-2): 1-6 (2011) - [j31]Michael L. Littman, Daniel M. Reeves:
Puzzle: baffling raffling. SIGecom Exch. 10(1): 39 (2011) - [c101]Christopher R. Mansley, Ari Weinstein, Michael L. Littman:
Sample-Based Planning for Continuous Action Markov Decision Processes. ICAPS 2011 - [c100]Michael Wunder, Michael Kaisers, John Robert Yaros, Michael L. Littman:
Using iterated reasoning to predict opponent strategies. AAMAS 2011: 593-600 - [c99]Sergiu Goschin, Michael L. Littman, David H. Ackley:
The effects of selection on noisy fitness optimization. GECCO 2011: 2059-2066 - [c98]Jordan T. Ash, Monica Babes, Gal Cohen, Sameen Jalal, Sam Lichtenberg, Michael L. Littman, Vukosi Marivate, Phillip Quiza, Blase Ur, Emily Zhang:
Scratchable Devices: User-Friendly Programming for Household Appliances. HCI (3) 2011: 137-146 - [c97]Monica Babes, Vukosi Marivate, Kaushik Subramanian, Michael L. Littman:
Apprenticeship Learning About Multiple Intentions. ICML 2011: 897-904 - [c96]John Asmuth, Michael L. Littman:
Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search. UAI 2011: 19-26 - [i11]Michael J. Kearns, Michael L. Littman, Satinder Singh, Peter Stone:
ATTac-2000: An Adaptive Autonomous Bidding Agent. CoRR abs/1106.0678 (2011) - [i10]János A. Csirik, Michael L. Littman, David A. McAllester, Robert E. Schapire, Peter Stone:
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. CoRR abs/1106.5270 (2011) - [i9]Nikos Vlassis, Michael L. Littman, David Barber:
On the computational complexity of stochastic controller optimization in POMDPs. CoRR abs/1107.3090 (2011) - 2010
- [j30]Lihong Li, Michael L. Littman:
Reducing reinforcement learning to KWIK online regression. Ann. Math. Artif. Intell. 58(3-4): 217-237 (2010) - [j29]Ali Nouri, Michael L. Littman:
Dimension reduction and its application to model-based exploration in continuous spaces. Mach. Learn. 81(1): 85-98 (2010) - [c95]Kaushik Subramanian, Michael L. Littman:
Efficient Apprenticeship Learning with Smart Humans. Enabling Intelligence through Middleware 2010 - [c94]Thomas J. Walsh, Sergiu Goschin, Michael L. Littman:
Integrating Sample-Based Planning and Model-Based Reinforcement Learning. AAAI 2010: 612-617 - [c93]Michael Wunder, Michael L. Littman, Michael Kaisers, John Robert Yaros:
A Cognitive Hierarchy Model Applied to the Lemonade Game. Interactive Decision Theory and Game Theory 2010 - [c92]Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk:
Generalizing Apprenticeship Learning across Hypothesis Classes. ICML 2010: 1119-1126 - [c91]Michael Wunder, Michael L. Littman, Monica Babes:
Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration. ICML 2010: 1167-1174 - [c90]Marie desJardins, Michael L. Littman:
Broadening student enthusiasm for computer science with a great insights course. SIGCSE 2010: 157-161 - [p1]Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins:
Learning Lexicographic Preference Models. Preference Learning 2010: 251-272
2000 – 2009
- 2009
- [j28]Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman:
Learning and planning in environments with delayed feedback. Auton. Agents Multi Agent Syst. 18(1): 83-105 (2009) - [j27]Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy:
Provably Efficient Learning with Typed Parametric Models. J. Mach. Learn. Res. 10: 1955-1988 (2009) - [j26]Alexander L. Strehl, Lihong Li, Michael L. Littman:
Reinforcement Learning in Finite MDPs: PAC Analysis. J. Mach. Learn. Res. 10: 2413-2444 (2009) - [c89]Lihong Li, Michael L. Littman, Christopher R. Mansley:
Online exploration in least-squares policy iteration. AAMAS (2) 2009: 733-739 - [c88]John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate:
A Bayesian Sampling Approach to Exploration in Reinforcement Learning. UAI 2009: 19-26 - [c87]Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman:
Exploring compact reinforcement-learning representations with linear regression. UAI 2009: 591-598 - [e1]Andrea Pohoreckyj Danyluk, Léon Bottou, Michael L. Littman:
Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009. ACM International Conference Proceeding Series 382, ACM 2009, ISBN 978-1-60558-516-1 [contents] - [r1]Carlos Diuk, Michael L. Littman:
Hierarchical Reinforcement Learning. Encyclopedia of Artificial Intelligence 2009: 825-830 - 2008
- [j25]David L. Roberts, Charles L. Isbell Jr., Michael L. Littman:
Optimization problems involving collections of dependent objects. Ann. Oper. Res. 163(1): 255-270 (2008) - [j24]Alexander L. Strehl, Michael L. Littman:
An analysis of model-based Interval Estimation for Markov Decision Processes. J. Comput. Syst. Sci. 74(8): 1309-1331 (2008) - [c86]John Asmuth, Michael L. Littman, Robert Zinkov:
Potential-based Shaping in Model-based Reinforcement Learning. AAAI 2008: 604-609 - [c85]Thomas J. Walsh, Michael L. Littman:
Efficient Learning of Action Schemas and Web-Service Descriptions. AAAI 2008: 714-719 - [c84]Monica Babes, Enrique Munoz de Cote, Michael L. Littman:
Social reward shaping in the prisoner's dilemma. AAMAS (3) 2008: 1389-1392 - [c83]Carlos Diuk, Andre Cohen, Michael L. Littman:
An object-oriented representation for efficient reinforcement learning. ICML 2008: 240-247 - [c82]Lihong Li, Michael L. Littman, Thomas J. Walsh:
Knows what it knows: a framework for self-aware learning. ICML 2008: 568-575 - [c81]Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman:
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML 2008: 752-759 - [c80]Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins:
Democratic approximation of lexicographic preference models. ICML 2008: 1200-1207 - [c79]Lihong Li, Michael L. Littman:
Efficient Value-Function Approximation via Online Linear Regression. ISAIM 2008 - [c78]Ali Nouri, Michael L. Littman:
Multi-resolution Exploration in Continuous Spaces. NIPS 2008: 1209-1216 - [c77]Michael L. Littman:
Autonomous Model Learning for Reinforcement Learning. QEST 2008: 3 - [c76]Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy:
CORL: A Continuous-state Offset-dynamics Reinforcement Learner. UAI 2008: 53-61 - [c75]Enrique Munoz de Cote, Michael L. Littman:
A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games. UAI 2008: 419-426 - 2007
- [j23]Martin Zinkevich, Amy Greenwald, Michael L. Littman:
A hierarchy of prescriptive goals for multiagent learning. Artif. Intell. 171(7): 440-447 (2007) - [j22]Amy Greenwald, Michael L. Littman:
Introduction to the special issue on learning and computational game theory. Mach. Learn. 67(1-2): 3-6 (2007) - [c74]Bethany R. Leffler, Michael L. Littman, Timothy Edmunds:
Efficient Reinforcement Learning with Relocatable Action Models. AAAI 2007: 572-577 - [c73]Alexander L. Strehl, Carlos Diuk, Michael L. Littman:
Efficient Structure Learning in Factored-State MDPs. AAAI 2007: 645-650 - [c72]Thomas J. Walsh, Michael L. Littman:
A Multiple Representation Approach to Learning Dynamical Systems. AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development 2007: 86-87 - [c71]Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman:
Planning and Learning in Environments with Delayed Feedback. ECML 2007: 442-453 - [c70]Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman:
Analyzing feature generation for value-function approximation. ICML 2007: 737-744 - [c69]Alexander L. Strehl, Michael L. Littman:
Online Linear Regression and Its Application to Model-Based Reinforcement Learning. NIPS 2007: 1417-1424 - 2006
- [c68]David L. Roberts, Mark J. Nelson, Charles Lee Isbell Jr., Michael Mateas, Michael L. Littman:
Targeting Specific Distributions of Trajectories in MDPs. AAAI 2006: 1213-1218 - [c67]Carlos Diuk, Michael L. Littman:
A Change Detection Model for Non-Stationary k-Armed Bandit Problems. AAAI Spring Symposium: Between a Rock and a Hard Place: Cognitive Science Principles Meet AI-Hard Problems 2006: 39 - [c66]Carlos Diuk, Alexander L. Strehl, Michael L. Littman:
A hierarchical approach to efficient reinforcement learning in deterministic domains. AAMAS 2006: 313-319 - [c65]Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman:
PAC model-free reinforcement learning. ICML 2006: 881-888 - [c64]Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh:
Experience-efficient learning in associative bandit problems. ICML 2006: 889-896 - [c63]Lihong Li, Thomas J. Walsh, Michael L. Littman:
Towards a Unified Theory of State Abstraction for MDPs. AI&M 2006 - [c62]Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich:
An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees. UAI 2006 - [c61]Alexander L. Strehl, Lihong Li, Michael L. Littman:
Incremental Model-based Learners With Formal Learning-Time Guarantees. UAI 2006 - 2005
- [j21]Nicholas L. Cassimatis, Sean Luke, Simon D. Levy, Ross W. Gayler, Pentti Kanerva, Chris Eliasmith, Timothy W. Bickmore, Alan C. Schultz, Randall Davis, James A. Landay, Robert C. Miller, Eric Saund, Thomas F. Stahovich, Michael L. Littman, Satinder Singh, Shlomo Argamon, Shlomo Dubnov:
Reports on the 2004 AAAI Fall Symposia. AI Mag. 26(1): 98-102 (2005) - [j20]Michael L. Littman, Peter Stone:
A polynomial-time Nash equilibrium algorithm for repeated games. Decis. Support Syst. 39(1): 55-66 (2005) - [j19]Håkan L. S. Younes, Michael L. Littman, David Weissman, John Asmuth:
The First Probabilistic Track of the International Planning Competition. J. Artif. Intell. Res. 24: 851-887 (2005) - [j18]Peter D. Turney, Michael L. Littman:
Corpus-based Learning of Analogies and Semantic Relations. Mach. Learn. 60(1-3): 251-278 (2005) - [c60]Lihong Li, Michael L. Littman:
Lazy Approximation for Solving Continuous Finite-Horizon MDPs. AAAI 2005: 1175-1180 - [c59]Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, Michael L. Littman:
Activity Recognition from Accelerometer Data. AAAI 2005: 1541-1546 - [c58]Alexander L. Strehl, Michael L. Littman:
A theoretical analysis of Model-Based Interval Estimation. ICML 2005: 856-863 - [c57]Martin Zinkevich, Amy Greenwald, Michael L. Littman:
Cyclic Equilibria in Markov Games. NIPS 2005: 1641-1648 - [c56]Bethany R. Leffler, Michael L. Littman, Alexander L. Strehl, Thomas J. Walsh:
Efficient Exploration With Latent Structure. Robotics: Science and Systems 2005: 81-88 - [i8]Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder:
Combining Independent Modules in Lexical Multiple-Choice Problems. CoRR abs/cs/0501018 (2005) - [i7]Peter D. Turney, Michael L. Littman:
Corpus-based Learning of Analogies and Semantic Relations. CoRR abs/cs/0508103 (2005) - 2004
- [c55]Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard E. Howard:
An Instance-Based State Representation for Network Repair. AAAI 2004: 287-292 - [c54]Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard E. Howard:
Reinforcement Learning for Autonomic Network Repair. ICAC 2004: 284-285 - [c53]Michael R. James, Satinder Singh, Michael L. Littman:
Planning with predictive state representations. ICMLA 2004: 304-311 - [c52]Alexander L. Strehl, Michael L. Littman:
An Empirical Evaluation of Interval Estimation for Markov Decision Processes. ICTAI 2004: 128-135 - 2003
- [j17]Stephen M. Majercik, Michael L. Littman:
Contingent planning under uncertainty via stochastic satisfiability. Artif. Intell. 147(1-2): 119-162 (2003) - [j16]Yukio Ohsawa, Peter McBurney, Simon Parsons, Christopher A. Miller, Alan C. Schultz, Jean Scholtz, Michael A. Goodrich, Eugene Santos Jr., Benjamin Bell, Charles Lee Isbell Jr., Michael L. Littman:
AAAI-2002 Fall Symposium Series. AI Mag. 24(1): 95-98 (2003) - [j15]Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester:
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. J. Artif. Intell. Res. 19: 209-242 (2003) - [j14]Peter D. Turney, Michael L. Littman:
Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst. 21(4): 315-346 (2003) - [c51]Michael L. Littman:
Tutorial: Learning Topics in Game-Theoretic Decision Making. COLT 2003: 1 - [c50]Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone:
Learning Predictive State Representations. ICML 2003: 712-719 - [c49]Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder:
Combining independent modules in lexical multiple-choice problems. RANLP 2003: 101-110 - [c48]Michael L. Littman, Peter Stone:
A polynomial-time nash equilibrium algorithm for repeated games. EC 2003: 48-54 - [i6]Peter D. Turney, Michael L. Littman:
Measuring Praise and Criticism: Inference of Semantic Orientation from Association. CoRR cs.CL/0309034 (2003) - [i5]Peter D. Turney, Michael L. Littman, Jeffrey Bigham, Victor Shnayder:
Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems. CoRR cs.CL/0309035 (2003) - [i4]Peter D. Turney, Michael L. Littman:
Learning Analogies and Semantic Relations. CoRR cs.LG/0307055 (2003) - 2002
- [j13]Michael L. Littman, Greg A. Keim, Noam M. Shazeer:
A probabilistic approach to solving crossword puzzles. Artif. Intell. 134(1-2): 23-55 (2002) - [c47]Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David A. McAllester:
ATTac-2001: A Learning, Autonomous Bidding Agent. AMEC 2002: 143-160 - [c46]Paul S. A. Reitsma, Peter Stone, János A. Csirik, Michael L. Littman:
Self-Enforcing Strategic Demand Reduction. AMEC 2002: 289-306 - [c45]Paul S. A. Reitsma, Peter Stone, János A. Csirik, Michael L. Littman:
Randomized strategic demand reduction: getting more by asking for less. AAMAS 2002: 162-163 - [c44]Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik:
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. ICML 2002: 546-553 - [c43]Michail G. Lagoudakis, Ronald Parr, Michael L. Littman:
Least-Squares Methods in Reinforcement Learning for Control. SETN 2002: 249-260 - [i3]Peter D. Turney, Michael L. Littman:
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus. CoRR cs.LG/0212012 (2002) - 2001
- [j12]Michael L. Littman:
Value-function reinforcement learning in Markov games. Cogn. Syst. Res. 2(1): 55-66 (2001) - [j11]Michail G. Lagoudakis, Michael L. Littman:
Learning to Select Branching Rules in the DPLL Procedure for Satisfiability. Electron. Notes Discret. Math. 9: 344-359 (2001) - [j10]Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns:
ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. 15: 189-206 (2001) - [j9]Michael L. Littman, Stephen M. Majercik, Toniann Pitassi:
Stochastic Boolean Satisfiability. J. Autom. Reason. 27(3): 251-296 (2001) - [c42]Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns:
ATTac-2000: an adaptive autonomous bidding agent. Agents 2001: 238-245 - [c41]Michael L. Littman, Peter Stone:
Implicit Negotiation in Repeated Games. ATAL 2001: 393-404 - [c40]Michael L. Littman:
Friend-or-Foe Q-learning in General-Sum Games. ICML 2001: 322-328 - [c39]Sanjoy Dasgupta, Michael L. Littman, David A. McAllester:
PAC Generalization Bounds for Co-training. NIPS 2001: 375-382 - [c38]Michael L. Littman, Michael J. Kearns, Satinder Singh:
An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS 2001: 817-823 - [c37]Michael L. Littman, Richard S. Sutton, Satinder Singh:
Predictive Representations of State. NIPS 2001: 1555-1561 - [c36]Fan Jiang, Michael L. Littman:
Approximate Dimension Reduction at NTCIR. NTCIR 2001 - [c35]Michael J. Kearns, Michael L. Littman, Satinder Singh:
Graphical Models for Game Theory. UAI 2001: 253-260 - [c34]János A. Csirik, Michael L. Littman, Satinder Singh, Peter Stone:
FAucS : An FCC Spectrum Auction Simulator for Autonomous Bidding Agents. WELCOM 2001: 139-151 - 2000
- [j8]Sebastian Thrun, Michael L. Littman:
A Review of Reinforcement Learning. AI Mag. 21(1): 103-105 (2000) - [j7]Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári:
Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Mach. Learn. 38(3): 287-308 (2000) - [c33]Michail G. Lagoudakis, Michael L. Littman:
Reinforcement Learning for Algorithm Selection. AAAI/IAAI 2000: 1081 - [c32]Jiefu Shi, Michael L. Littman:
Towards Approximately Optimal Poker. AAAI/IAAI 2000: 1094 - [c31]Jiefu Shi, Michael L. Littman:
Abstraction Methods for Game Theoretic Poker. Computers and Games 2000: 333-345 - [c30]Michael L. Littman:
Review: Computer Language Games. Computers and Games 2000: 396-404 - [c29]Fan Jiang, Michael L. Littman:
Approximate Dimension Equalization in Vector-based Information Retrieval. ICML 2000: 423-430 - [c28]Michail G. Lagoudakis, Michael L. Littman:
Algorithm Selection using Reinforcement Learning. ICML 2000: 511-518 - [c27]Justin A. Boyan, Michael L. Littman:
Exact Solutions to Time-Dependent MDPs. NIPS 2000: 1026-1032
1990 – 1999
- 1999
- [j6]Giuseppe De Giacomo, Marie desJardins, Dolores Cañamero, Glenn S. Wasson, Michael L. Littman, Gerard Allwein, Kim Marriott, Bernd Meyer, Barbara Webb, Tom Con:
The AAAI Fall Symposia. AI Mag. 20(3): 87-89 (1999) - [j5]Csaba Szepesvári, Michael L. Littman:
A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms. Neural Comput. 11(8): 2017-2060 (1999) - [c26]Noam M. Shazeer, Michael L. Littman, Greg A. Keim:
Solving Crossword Puzzles as Probabilistic Constraint Satisfaction. AAAI/IAAI 1999: 156-162 - [c25]Stephen M. Majercik, Michael L. Littman:
Contingent Planning Under Uncertainty via Stochastic Satisfiability. AAAI/IAAI 1999: 549-556 - [c24]Michael L. Littman:
Initial Experiments in Stochastic Satisfiability. AAAI/IAAI 1999: 667-672 - [c23]Greg A. Keim, Noam M. Shazeer, Michael L. Littman, Sushant Agarwal, Catherine M. Cheves, Joseph Fitzgerald, Jason Grosland, Fan Jiang, Shannon Pollard, Karl Weinmeister:
PROVERB: The Probabilistic Cruciverbalist. AAAI/IAAI 1999: 710-717 - [c22]Michael L. Littman, Greg A. Keim, Noam M. Shazeer:
Solving Crosswords with PROVERB. AAAI/IAAI 1999: 914-915 - 1998
- [j4]Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra:
Planning and Acting in Partially Observable Stochastic Domains. Artif. Intell. 101(1-2): 99-134 (1998) - [j3]Michael L. Littman, Judy Goldsmith, Martin Mundhenk:
The Computational Complexity of Probabilistic Planning. J. Artif. Intell. Res. 9: 1-36 (1998) - [c21]Stephen M. Majercik, Michael L. Littman:
Using Caching to Solve Larger Probabilistic Planning Problems. AAAI/IAAI 1998: 954-959 - [c20]Stephen M. Majercik, Michael L. Littman:
MAXPLAN: A New Approach to Probabilistic Planning. AIPS 1998: 86-93 - [c19]Michael L. Littman, Fan Jiang, Greg A. Keim:
Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus. ICML 1998: 314-322 - [i2]Michael L. Littman, Judy Goldsmith, Martin Mundhenk:
The Computational Complexity of Probabilistic Planning. CoRR cs.AI/9808101 (1998) - 1997
- [c18]Michael L. Littman:
Probabilistic Propositional Planning: Representations and Complexity. AAAI/IAAI 1997: 748-754 - [c17]Michael S. Fulkerson, Michael L. Littman, Greg A. Keim:
Speeding Safely: Multi-Criteria Optimization in Probabilistic Planning. AAAI/IAAI 1997: 831 - [c16]Bob Rehder, Michael L. Littman, Susan T. Dumais, Thomas K. Landauer:
Automatic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing. TREC 1997: 233-239 - [c15]Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang:
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes. UAI 1997: 54-61 - [c14]Judy Goldsmith, Michael L. Littman, Martin Mundhenk:
The Complexity of Plan Existence and Evaluation in Probabilistic Domains. UAI 1997: 182-189 - 1996
- [j2]Eugene Charniak, Glenn Carroll, John E. Adcock, Anthony R. Cassandra, Yoshihiko Gotoh, Jeremy Katz, Michael L. Littman, John McCann:
Taggers for Parsers. Artif. Intell. 85(1-2): 45-57 (1996) - [j1]Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore:
Reinforcement Learning: A Survey. J. Artif. Intell. Res. 4: 237-285 (1996) - [c13]Michael L. Littman, Csaba Szepesvári:
A Generalized Reinforcement-Learning Model: Convergence and Applications. ICML 1996: 310-318 - [i1]Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore:
Reinforcement Learning: A Survey. CoRR cs.AI/9605103 (1996) - 1995
- [c12]Michael L. Littman, Anthony R. Cassandra, Leslie Pack Kaelbling:
Learning Policies for Partially Observable Environments: Scaling Up. ICML 1995: 362-370 - [c11]Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra:
Partially Observable Markov Decision Processes for Artificial Intelligence. KI 1995: 1-17 - [c10]Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra:
Partially Observable Markov Decision Processes for Artificial Intelligence. Reasoning with Uncertainty in Robotics 1995: 146-163 - [c9]Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling:
On the Complexity of Solving Markov Decision Problems. UAI 1995: 394-402 - 1994
- [c8]Anthony R. Cassandra, Leslie Pack Kaelbling, Michael L. Littman:
Acting Optimally in Partially Observable Stochastic Domains. AAAI 1994: 1023-1028 - [c7]Michael L. Littman:
Markov Games as a Framework for Multi-Agent Reinforcement Learning. ICML 1994: 157-163 - 1993
- [c6]Robert B. Allen, Pascal Obry, Michael L. Littman:
An interface for navigating clustered document sets returned by queries. COOCS 1993: 166-171 - [c5]Justin A. Boyan, Michael L. Littman:
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. NIPS 1993: 671-678 - 1992
- [c4]Laurence Brothers, James D. Hollan, Jakob Neilsen, Scott Stornetta, Steven P. Abney, George W. Furnas, Michael L. Littman:
Supporting Informal Communication via Ephemeral Interest Groups. CSCW 1992: 84-90 - 1991
- [c3]Dennis E. Egan, Michael Lesk, R. Daniel Ketchum, Carol C. Lochbaum, Joel R. Remde, Michael L. Littman, Thomas K. Landauer:
Hypertext for the Electronic Library? CORE Sample Results. Hypertext 1991: 299-312 - [c2]Michael L. Littman, David H. Ackley:
Adaptation in Constant Utility Non-Stationary Environments. ICGA 1991: 136-142
1980 – 1989
- 1989
- [c1]David H. Ackley, Michael L. Littman:
Generalization and Scaling in Reinforcement Learning. NIPS 1989: 550-557
Coauthor Index
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