Michael L. Littman

About

Michael L. Littman is a University Professor of Computer Science and serves as the Associate Provost for Artificial Intelligence at Brown University. His primary research areas include Artificial Intelligence, Machine Learning, Reinforcement Learning, and Robotics, with secondary focus on Algorithmic Fairness. Dr. Littman's work involves advancing the understanding and development of intelligent systems, contributing to the fields of AI and machine learning through research and leadership. His role at Brown encompasses both academic and administrative responsibilities, supporting the university's initiatives in artificial intelligence and related disciplines.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Sociology
• Political Science
• Psychology
• Algorithm
• Management science
• Mathematical optimization
• Cognitive psychology

Selected publications

• Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report

  arXiv (Cornell University) · 2022 · 142 citations

  1st authorCorresponding

  In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled "Gathering Strength, Gathering Storms," answers a set of 14 questions probing critical areas of AI development address…

  PublisherOA PDFDOI

• People construct simplified mental representations to plan

  Nature · 2022 · 115 citations

  DOI

• A domain-agnostic approach for characterization of lifelong learning systems

  Neural Networks · 2023-01-20 · 16 citations

  articleOpen access
  PublisherOA PDFDOI

• Software Engineering of Machine Learning Systems

  Communications of the ACM · 2023-01-20 · 11 citations

  article

  Seeking to make machine learning more dependable.

  PublisherDOI

• Helping Users Debug Trigger-Action Programs

  Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2022-12-21 · 11 citations

  articleOpen access

  Trigger-action programming (TAP) empowers a wide array of users to automate Internet of Things (IoT) devices. However, it can be challenging for users to create completely correct trigger-action programs (TAPs) on the first try, necessitating debugging. While TAP has received substantial research attention, TAP debugging has not. In this paper, we present the first empirical study of users' end-to-end TAP debugging process, focusing on obstacles users face in debugging TAPs and how well users ul…

  PublisherOA PDFDOI

Recent grants

• RI: Small: Understanding Value-based Multiagent Learning and Its Applications

  NSF · $157k · 2013–2016

• RI: Small: Collaborative Research: Speeding Up Learning through Modeling the Pragmatics of Training

  NSF · $156k · 2013–2016

• HSD-DRU: The Role of Communication in the Dynamics of Effective Decision Making

  NSF · $685k · 2007–2011

Frequent coauthors

• James MacGlashan

  56 shared

• Mark K. Ho

  New York University

  41 shared

• David Abel

  38 shared

• Kavosh Asadi

  35 shared

• Leslie Pack Kaelbling

  32 shared

• George Konidaris

  John Brown University

  29 shared

• Dilip Arumugam

  Stanford University

  27 shared

• Stefanie Tellex

  John Brown University

  22 shared

Education

• Ph.D., Computer Science

  Brown University

  1991

• M.S., Computer Science

  Brown University

  1987

• B.S., Computer Science

  Brown University

  1985

Similar researchers at Brown University

• Maurice P Herlihyh-72
• Roberto Tamassiah-69
• Eli Upfalh-66