2026 Nathan R. Sturtevant: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	35	11547	11216	452	433	215	6679

Overview

Nathan R. Sturtevant is a researcher affiliated with the University of Alberta in Canada. Their work primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence and related subfields.

The main subfields of study in which Nathan R. Sturtevant has contributed include:

Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Signal Processing
Sociology and Political Science

The researcher's work covers several core topics within these areas, such as:

Robotic Path Planning Algorithms
Artificial Intelligence in Games
AI-based Problem Solving and Planning
Constraint Satisfaction and Optimization
Metaheuristic Optimization Algorithms Research
Optimization and Search Problems
Data Management and Algorithms

Nathan R. Sturtevant has authored numerous papers, with recent publications including:

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks (2021), Proceedings of the International Symposium on Combinatorial Search
Search-Based Optimal Solvers for the Multi-Agent Pathfinding Problem: Summary and Challenges (2021), Proceedings of the International Symposium on Combinatorial Search
Conflict-Based Search For Optimal Multi-Agent Path Finding (2021), Proceedings of the AAAI Conference on Artificial Intelligence
Conflict-Based Search for Optimal Multi-Agent Path Finding (2021), Proceedings of the International Symposium on Combinatorial Search
A Polynomial-Time Algorithm for Non-Optimal Multi-Agent Pathfinding (2021), Proceedings of the International Symposium on Combinatorial Search

Frequent collaborators in their research include:

Ariel Felner
Shahaf Shperberg
Matthew Guzdial
Guni Sharon
Roni Stern

Nathan R. Sturtevant regularly publishes in venues such as:

Proceedings of the International Symposium on Combinatorial Search
arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Proceedings of the International Conference on Automated Planning and Scheduling
Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

Conflict-based search for optimal multi-agent pathfinding

Guni Sharon;Roni Stern;Ariel Felner;Nathan R. Sturtevant

1287
Citations
Benchmarks for Grid-Based Pathfinding

N. R. Sturtevant

655
Citations
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Roni Stern;Nathan R. Sturtevant;Ariel Felner;Sven Koenig

294
Citations
An Analysis of UCT in Multi-player Games

Nathan R. Sturtevant

269
Citations
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

Roni Stern;Nathan Sturtevant;Ariel Felner;Sven Koenig

208
Citations
Search-Based Optimal Solvers for the Multi-Agent Pathfinding Problem: Summary and Challenges

Ariel Felner;Roni Stern;Solomon Eyal Shimony;Eli Boyarski

199
Citations
Partial pathfinding using map abstraction and refinement

Nathan Sturtevant;Michael Buro

193
Citations
Enhanced partial expansion A

Meir Goldenberg;Ariel Felner;Roni Stern;Guni Sharon

175
Citations
Understanding the success of perfect information monte carlo sampling in game tree search

Jeffrey Long;Nathan R. Sturtevant;Michael Buro;Timothy Furtak

121
Citations
Memory-based heuristics for explicit state spaces

Nathan R. Sturtevant;Ariel Felner;Max Barrer;Jonathan Schaeffer

109
Citations
Graph abstraction in real-time heuristic search

Vadim Bulitko;Nathan Sturtevant;Jieshan Lu;Timothy Yau

107
Citations
On Pruning Techniques for Multi-Player Games

Nathan R. Sturtevant;Richard E. Korf

100
Citations
Conflict-based search for optimal multi-agent path finding

Guni Sharon;Roni Stern;Ariel Felner;Nathan Sturtevant

99
Citations
Improving state evaluation, inference, and search in trick-based card games

Michael Buro;Jeffrey R. Long;Timothy Furtak;Nathan Sturtevant

91
Citations
Memory-efficient abstractions for pathfinding

Nathan R. Sturtevant

91
Citations
A Polynomial-Time Algorithm for Non-Optimal Multi-Agent Pathfinding

Mokhtar M. Khorshid;Robert C. Holte;Nathan R. Sturtevant

90
Citations
Partial-expansion A* with selective node generation

Ariel Felner;Meir Goldenberg;Guni Sharon;Roni Stern

85
Citations
Improving collaborative pathfinding using map abstraction

Nathan Sturtevant;Michael Buro

85
Citations
Meta-Agent Conflict-Based Search For Optimal Multi-Agent Path Finding

Guni Sharon;Roni Stern;Ariel Felner;Nathan R. Sturtevant

81
Citations
Feature construction for reinforcement learning in hearts

Nathan R. Sturtevant;Adam M. White

75
Citations
A* search with inconsistent heuristics

Zhifu Zhang;Nathan R. Sturtevant;Robert Holte;Jonathan Schaeffer

23
Citations

Frequent Co-Authors

Ariel Felner Ben-Gurion University of the Negev

Jonathan Schaeffer University of Alberta

Robert C. Holte University of Alberta

Sven Koenig University of Southern California

Malte Helmert University of Basel

Michael Bowling University of Alberta

Richard E. Korf University of California, Los Angeles

Maxim Likhachev Carnegie Mellon University

Jeffrey S. Rosenschein Hebrew University of Jerusalem

Julian Togelius New York University

External Links

Personal website of Nathan R. Sturtevant

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