Ariel Felner
Papers
8
Total Citations
412
H-Index
5
About
Ariel Felner is a leading figure in artificial intelligence, renowned for his foundational contributions to heuristic search and multi-agent pathfinding (MAPF). His work has fundamentally shaped how autonomous systems plan collision-free paths, with direct applications in automated warehouses, robotics, and logistics. Felner’s seminal survey, "Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks" (2021), with 276 citations, serves as the definitive reference for the field, establishing the core problem definitions and evaluation standards. He has also pioneered advanced search algorithms, such as the Extended Increasing Cost Tree Search for non-unit cost domains (2018, 59 citations), which expanded optimal MAPF solutions beyond simple grids. His innovative concept of "swamp hierarchies" (2010, 35 citations) introduced a powerful technique to dramatically reduce search spaces by identifying and pruning irrelevant graph regions, accelerating pathfinding in complex networks like computer games and transportation. With over 400 total citations, Felner’s work is distinguished by its practical impact and theoretical depth, consistently pushing the boundaries of efficient, bounded-cost search and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks276 citations · 2021
- 2Extended Increasing Cost Tree Search for Non-Unit Cost Domains59 citations · 2018
- 3Search Space Reduction Using Swamp Hierarchies35 citations · 2010
- 4
- 5Search Space Reduction Using Swamp Hierarchies11 citations · 2010
- 6Conflict-tolerant and conflict-free multi-agent meeting3 citations · 2023
- 7Ants meeting algorithms3 citations · 2010
- 8The compressed differential heuristic2 citations · 2017