no code implementations • 19 Apr 2024 • Douglas Rebstock, Christopher Solinas, Nathan R. Sturtevant, Michael Buro
Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large.
no code implementations • 26 Dec 2023 • Thayne T. Walker, Nathan R. Sturtevant, Ariel Felner
While the study of unit-cost Multi-Agent Pathfinding (MAPF) problems has been popular, many real-world problems require continuous time and costs due to various movement models.
no code implementations • 30 Jul 2019 • Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant
For graph search, A* can require $\Omega(2^{n})$ expansions, where $n$ is the number of states within the final $f$ bound.
no code implementations • 27 May 2019 • Douglas Rebstock, Christopher Solinas, Michael Buro, Nathan R. Sturtevant
Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions.
1 code implementation • 10 Mar 2017 • Jingwei Chen, Robert C. Holte, Sandra Zilles, Nathan R. Sturtevant
pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions.
no code implementations • 2 Jun 2014 • Marc Lanctot, Mark H. M. Winands, Tom Pepels, Nathan R. Sturtevant
In recent years, combining ideas from traditional minimax search in MCTS has been shown to be advantageous in some domains, such as Lines of Action, Amazons, and Breakthrough.