World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
19786
World Ranking
3355
National Ranking
1624

Research.com Recognitions

  • 2011 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2006 - ACM Fellow For contributions to the development and application of cognitive architectures.
  • 1995 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For contributions to machine learning, integrated architectures for intelligence, and unified theories of cognition.

Overview

John E. Laird is affiliated with the University of Michigan-Ann Arbor in the United States. Their research primarily falls within the field of Computer Science, with a strong focus on Artificial Intelligence. Other subfields contributing to their work include Cognitive Neuroscience, General Health Professions, Computer Vision and Pattern Recognition, and Information Systems.

The scientist's research covers various topics such as AI-based Problem Solving and Planning, Topic Modeling, Natural Language Processing Techniques, Multi-Agent Systems and Negotiation, Semantic Web and Ontologies, Speech and Dialogue Systems, and Multimodal Machine Learning Applications.

Frequent coauthors collaborating with John E. Laird include Robert E. Wray, James R. Kirk, Steven J. Jones, Peter Lindes, and Christian Lebière.

Key publication venues where their work appears regularly are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the AAAI Symposium Series
  • Journal of Artificial General Intelligence
  • NeuroImage

Selected recent papers by John E. Laird include:

  • Special Issue "On Defining Artificial Intelligence"-Commentaries and Author's Response, 2020, Journal of Artificial General Intelligence
  • Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains, 2021, NeuroImage
  • Acquiring Grounded Representations of Words with Situated Interactive Instruction, 2025, arXiv (Cornell University)
  • Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis, 2024, Proceedings of the AAAI Conference on Artificial Intelligence
  • An Analysis and Comparison of ACT-R and Soar, 2022, arXiv (Cornell University)

John E. Laird has been recognized as a fellow of multiple professional organizations. These include the American Association for the Advancement of Science (AAAS) since 2011, the ACM Fellow in 2006 for contributions to cognitive architectures, and the Association for the Advancement of Artificial Intelligence (AAAI) since 1995 for work in machine learning, integrated architectures for intelligence, and unified theories of cognition.

Best Publications

  • Soar: an architecture for general intelligence

    J. E. Laird;A. Newell;P. S. Rosenbloom

  • The Soar Cognitive Architecture

    John E. Laird

  • Cognitive architectures: Research issues and challenges

    Pat Langley;John E. Laird;Seth Rogers

  • Chunking in Soar: the anatomy of a general learning mechanism

    John E. Laird;Paul S. Rosenbloom;Allen Newell

  • Human-Level AI's Killer Application: Interactive Computer Games

    John Laird;Michael VanLent

  • Extending the Soar Cognitive Architecture

    John E. Laird

  • Human-Level AI's Killer Application: Interactive Computer Games

    John E. Laird;Michael van Lent

  • Automated Intelligent Pilots for Combat Flight Simulation

    Randolph M. Jones;John E. Laird;Paul E. Nielsen;Karen J. Coulter

  • Intelligent Agents for Interactive Simulation Environments

    Milind Tambe;W. Lewis Johnson;Randolph M. Jones;Frank V. Koss

  • A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics

    John E. Laird;Christian Lebiere;Paul S. Rosenbloom

  • Symbolic architectures for cognition

    Allen Newell;Paul S. Rosenbloom;John E. Laird

  • Integrating execution, planning, and learning in Soar for external environments

    J. E. Laird;P. S. Rosenbloom

  • The soar papers : research on integrated intelligence

    Paul S. Rosenbloom;John Laird;Allen Newell

  • It knows what you're going to do: adding anticipation to a Quakebot

    John E. Laird

  • Universal Subgoaling and Chunking: The Automatic Generation and Learning of Goal Hierarchies

    John Laird;Paul Rosenbloom;Allen Newell

  • A preliminary analysis of the Soar architecture as a basis for general intelligence

    Paul S. Rosenbloom;John E. Laird;Allen Newell;Robert McCarl

  • Using a computer game to develop advanced AI

    J.E. Laird

  • A Gentle Introduction to Soar, an Architecture for Human Cognition.

    Jill F. Lehman;John Laird;Paul Rosenbloom

  • Stimulus-Response Compatibility

    John Laird;Paul Rosenbloom;Allen Newell

  • AI characters and directors for interactive computer games

    Brian Magerko;John E. Laird;Mazin Assanie;Alex Kerfoot

  • Soar Papers: Research on Integrated Intelligence

    Paul S. Rosenbloom;Allen Newell;John E. Laird

Frequent Co-Authors

Paul S. Rosenbloom
Paul S. Rosenbloom University of Southern California
Allen Newell
Allen Newell Carnegie Mellon University
Christian Lebiere
Christian Lebiere Carnegie Mellon University
Michael Hucka
Michael Hucka California Institute of Technology
Richard L. Lewis
Richard L. Lewis University of Michigan–Ann Arbor
Kenneth D. Forbus
Kenneth D. Forbus Northwestern University
Usama M. Fayyad
Usama M. Fayyad Open Insights
Pat Langley
Pat Langley Stanford University
Milind Tambe
Milind Tambe Harvard University
Ying Zhao
Ying Zhao Nankai University

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