Peter Yap

About

Peter Yap is a leading researcher in artificial intelligence and computational pathfinding, whose work has fundamentally shaped how autonomous systems navigate grid-based environments. His research focuses on developing efficient abstraction techniques and search algorithms for pathfinding on maps, a critical challenge in robotics, video games, and geographic information systems. Yap’s seminal 2002 paper, "Grid-Based Path-Finding," which has garnered 122 citations, established foundational methods for representing and searching grid worlds. He further advanced the field with his 2003 comparative study of grid abstractions, cited 35 times, where he systematically evaluated different approaches to reduce state-space complexity while preserving optimality. This work demonstrated that clever abstraction can dramatically accelerate A* and IDA* searches without sacrificing solution quality. Yap’s contributions are particularly notable for bridging theoretical rigor with practical application—his techniques are widely used in commercial game engines and robotic navigation systems. By providing clear benchmarks and comparative analyses, he has enabled subsequent researchers to make informed design choices, cementing his influence on both academic AI and real-world autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Grid-Based Path-Finding

   122 citations · 2002
2. 2
   Comparison of different grid abstractions for pathfinding on maps

   35 citations · 2003

Key Collaborators

Contact & Links