William Yeoh

New Mexico State University, University of Southern California, Washington University in St. Louis

Papers

Total Citations

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About

William Yeoh is a leading researcher in artificial intelligence, with key contributions spanning goal recognition design, multi-agent path finding (MAPF), and human-robot interaction. His work on goal recognition design, as detailed in his highly cited 2016 paper (31 citations), pioneered methods for modifying agent environments to force early goal revelation, advancing the field's theoretical foundations. In robotics, his 2010 paper on Generalized Fringe-Retrieving A* (22 citations) revolutionized moving target search for unmanned ground vehicles by integrating incremental search techniques, enabling faster hunter-follower dynamics on state lattices. Yeoh also developed ros-dmapf (2019, 18 citations), a distributed solver that addresses the scalability challenges of centralized MAPF approaches, offering a practical tradeoff between completeness and performance for multi-robot systems. More recently, his 2020 work on user annoyance-aware preference elicitation (3 citations) has broken new ground in social robotics, formulating frameworks that balance information gathering with user tolerance thresholds. With over 70 total citations across his most influential works, Yeoh's research continues to shape AI-driven robotics, bridging theoretical algorithms with real-world deployment challenges.