Abstract
This paper presents Monte-Carlo Fork Search (MCFS), a new algorithm that solves Cooperative Path-Finding (CPF) problems with simultaneity. The background is Monte-Carlo Tree Search (MCTS) and Nested Monte-Carlo Search (NMCS). Concerning CPF, MCFS avoids to enter into the curse of the very high branching factor. Regarding MCTS, the key idea of MCFS is to build a tree balanced over the whole game tree. To do so, after a simulation, MCFS stores the whole sequence of actions in the tree, which enables MCFS to fork new sequences at any depth in the built tree. This idea fits CPF problems in which the branching factor is too large for MCTS or A* approaches, and in which congestion may arise at any distance from the start state. With sufficient time and memory, Nested MCFS (NMCFS) solves congestion problems in the literature finding better solutions than the state-of-the-art solutions, and it solves N-puzzles without hole near-optimally. The algorithm is anytime and complete. The scalability of the approach is shown for gridsize up to \(200\times 200\) and up to \(400\) agents.
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Bouzy, B. (2014). Monte-Carlo Fork Search for Cooperative Path-Finding. In: Cazenave, T., Winands, M., Iida, H. (eds) Computer Games. CGW 2013. Communications in Computer and Information Science, vol 408. Springer, Cham. https://doi.org/10.1007/978-3-319-05428-5_1
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