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ORCIDMichael L. Littman
Person information
- affiliation: Brown University, Department of Computer Science
- 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
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2020 – today
- 2023
[j53]Charles Isbell, Michael L. Littman, Peter Norvig:
Software Engineering of Machine Learning Systems. Commun. ACM 66(2): 35-37 (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 L. 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 L. 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 L. Littman:
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. AISTATS 2023: 1390-1410 - [c167]Haotian Fu, Shangqun Yu, Saket Tiwari, Michael L. 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 L. 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 L. 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 L. 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 L. Littman:
Meta-Learning Transferable Parameterized Skills. CoRR abs/2206.03597 (2022) - [i70]Haotian Fu, Shangqun Yu, Michael L. 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 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 L. 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 L. 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 L. 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 L. 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 L. 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 L. 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, Sophie Yue 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]