most citedMonte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

6 citations · 6 across the 1 of their papers we have counts for

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cs.AI2025

Online Submission and Evaluation System Design for Competition Operations

Zhe Chen, Daniel Harabor, Ryan Hechnenberger +1

Research communities have developed benchmark datasets across domains to compare the performance of algorithms and techniques However, tracking the progress in these research areas…

cs.AI2024

Set-Based Retrograde Analysis: Precomputing the Solution to 24-card Bridge Double Dummy Deals

Isaac Stone, Nathan R. Sturtevant, Jonathan Schaeffer

Retrograde analysis is used in game-playing programs to solve states at the end of a game, working backwards toward the start of the game. The algorithm iterates through and comput…

cs.AI2024

On the Completeness of Conflict-Based Search: Temporally-Relative Duplicate Pruning

Thayne T Walker, Nathan R Sturtevant

Conflict-Based Search (CBS) algorithm for the multi-agent pathfinding (MAPF) problem is that it is incomplete for problems which have no solution; if no mitigating procedure is run…

cs.AI2024

Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games

Douglas Rebstock, Christopher Solinas, Nathan R. Sturtevant +1

Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large. This challe…

cs.AI20146 cited

Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

Marc Lanctot, Mark H. M. Winands, Tom Pepels +1

Monte Carlo Tree Search (MCTS) has improved the performance of game engines in domains such as Go, Hex, and general game playing. MCTS has been shown to outperform classic alpha-be…