Michael L. Littman Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Professor

John Brown University

faculty

76 h-index 386 pubs 38,851 cited

Research Areas

Reinforcement Learning in Robotics Machine Learning and Algorithms Bayesian Modeling and Causal Inference AI-based Problem Solving and Planning Evolutionary Algorithms and Applications

Links

ORCID

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OverviewAI-generated summary

Michael Littman is a professor at John Brown University whose research encompasses a broad range of artificial intelligence subfields, including reinforcement learning in robotics, machine learning algorithms, Bayesian modeling, causal inference, AI-based problem solving, and evolutionary algorithms. Littman's work touches on both theoretical foundations and practical applications of AI. His contributions include studies of how people create simplified mental representations for planning and the planning and interpretation of communicative demonstrations. He co-authored a 2022 report on the One Hundred Year Study on Artificial Intelligence (AI100). He also explores issues of integrity within computer science research.

Metrics

  • h-index: 76
  • Publications: 386
  • Citations: 38,851

Selected Publications

  • Enabling End Users to Program Robots Using Reinforcement Learning (2025) DOI
  • Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable (2023) DOI
  • Selecting Context Clozes for Lightweight Reading Compliance (2022) DOI
  • Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data (2022) DOI
  • On the Expressivity of Markov Reward (Extended Abstract) (2022) DOI
  • On the (In)Tractability of Reinforcement Learning for LTL Objectives (2022) DOI
  • Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator (2021) DOI
  • Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract) (2021) DOI
  • Understanding Trigger-Action Programs Through Novel Visualizations of Program Differences (2021) DOI
  • Deep Radial-Basis Value Functions for Continuous Control (2021) DOI
  • Lipschitz Lifelong Reinforcement Learning (2021) DOI

Collaborators

Researchers in the database who share publications

George Konidaris John Brown University 5 shared publications Carl Trimbach John Brown University 2 shared publications Kweku Kwegyir-Aggrey John Brown University 1 shared publication Ellie Pavlick John Brown University 1 shared publication Steven A. Sloman John Brown University 1 shared publication

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