About

Pat Langley’s research lies at the intersection of artificial intelligence, cognitive systems, and robotics, with a particular focus on how intelligent agents perceive, learn, and interact with dynamic environments. A central contribution is his pioneering work on place recognition and place learning for mobile robots, where he developed techniques that associate evidence grids with distinct locations and use hill-climbing alignment for robust recognition—even in changing surroundings. This foundational work, first published in 1997 and accumulating over 80 citations, has influenced spatial reasoning and autonomous navigation. Langley also made significant strides in explainable agency, co-authoring a highly cited 2017 paper (153 citations) that addresses the critical need for transparency in intelligent autonomous systems. His research on communicating and executing high-level instructions for human-robot interaction, along with the integrated Magellan architecture for mobile robotics, demonstrates a sustained commitment to building systems that are both adaptive and interpretable. Through his work on case-based learning, interactive cognitive systems, and social intelligence, Langley has shaped how researchers think about machines that learn, reason, and collaborate with people in real-world settings.

Research Focus

Key Achievements

6
H-Index
11
Papers
293
Total Citations
27
Avg Citations/Paper
🏆 Most Cited Paper
Explainable Agency for Intelligent Autonomous Systems
153 citations · 2017
📈 Most Prolific Year: 1997 (3 Papers)
🤝 Key Collaborators: 12
🏛 Institutions: University of Auckland, Stanford University, Institute for the Study of Learning and Expertise, Arizona State University, Laboratoire d'Informatique de Paris-Nord, Institute For Defense Analyses

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago