| 2025 | ACL | Do Language Models Understand the Cognitive Tasks Given to Them? Investigations with the N-Back Paradigm. | Xiaoyang Hu, Richard L. Lewis |
| 2024 | CogSci | Regret Theory predicts decoy effects in risky and multiattribute choice. | Logan A. Walls, Andrew Howes, Richard L. Lewis |
| 2023 | ACL | In-Context Analogical Reasoning with Pre-Trained Language Models. | Xiaoyang Hu, Shane Storks, Richard L. Lewis, Joyce Chai |
| 2023 | ICLR | Composing Task Knowledge With Modular Successor Feature Approximators. | Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh |
| 2022 | AAAI | Adaptive Pairwise Weights for Temporal Credit Assignment. | Zeyu Zheng, Risto Vuorio, Richard L. Lewis, Satinder Singh |
| 2021 | ICML | Reinforcement Learning of Implicit and Explicit Control Flow Instructions. | Ethan Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh |
| 2021 | IJCAI | Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment. | Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh |
| 2020 | AAAI | How Should an Agent Practice? | Janarthanan Rajendran, Richard L. Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh |
| 2019 | AAAI | Learning to Communicate and Solve Visual Blocks-World Tasks. | Qi Zhang, Richard L. Lewis, Satinder Singh, Edmund H. Durfee |
| 2017 | CogSci | Human Visual Search as a Deep Reinforcement Learning Solution to a POMDP. | Aditya Acharya, Xiuli Chen, Christopher W. Myers, Richard L. Lewis, Andrew Howes |
| 2016 | IJCAI | Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games. | Xiaoxiao Guo, Satinder Singh, Richard L. Lewis, Honglak Lee |
| 2016 | IJCAI | The Dependence of Effective Planning Horizon on Model Accuracy. | Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis |
| 2013 | CogSci | Bounded Optimal State Estimation and Control in Visual Search: Explaining Distractor Ratio Effects. | Christopher W. Myers, Richard L. Lewis, Andrew Howes |
| 2013 | HCI | Linking Context to Evaluation in the Design of Safety Critical Interfaces. | Michael Feary, Dorrit Billman, Xiuli Chen, Andrew Howes, Richard L. Lewis, Lance Sherry, Satinder Singh |
| 2012 | AAMAS | Strong mitigation: nesting search for good policies within search for good reward. | Jeshua Bratman, Satinder Singh, Jonathan Sorg, Richard L. Lewis |
| 2011 | AAAI | Optimal Rewards versus Leaf-Evaluation Heuristics in Planning Agents. | Jonathan Sorg, Satinder Singh, Richard L. Lewis |
| 2010 | ICML | Internal Rewards Mitigate Agent Boundedness. | Jonathan Sorg, Satinder Singh, Richard L. Lewis |
| 2010 | UAI | Variance-Based Rewards for Approximate Bayesian Reinforcement Learning. | Jonathan Sorg, Satinder Singh, Richard L. Lewis |
| 2006 | CHI | Generating automated predictions of behavior strategically adapted to specific performance objectives. | Katherine Eng, Richard L. Lewis, Irene Tollinger, Alina Chu, Andrew Howes, Alonso H. Vera |
| 2005 | CHI | Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modeling. | Irene Tollinger, Richard L. Lewis, Michael McCurdy, Preston Tollinger, Alonso H. Vera, Andrew Howes, Laura Pelton |
| 2004 | CHI | A constraint satisfaction approach to predicting skilled interactive cognition. | Alonso H. Vera, Andrew Howes, Michael McCurdy, Richard L. Lewis |