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Control of non player characters in a medical learning game with Monte Carlo Tree Search (poster)

Abstract : In this paper, we apply the Monte Carlo Tree Search (MCTS) method for controlling at once several virtual characters in a 3D multi-player learning game. The MCTS is used as a search algorithm to explore a search space where every potential solution reflects a specific state of the game environment. Functions representing the interaction abilities of each character are provided to the algorithm to leap from one state to another. We show that the MCTS algorithm successfully manages to plan the actions for several virtual characters in a synchronized fashion, from the initial state to one or more desirable end states. Besides, we demonstrate the ability of this algorithm to fulfill two specific requirements of a learning game AI : guiding the non player characters to follow a predefined plan while coping with the unpredictability of the human players actions.
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https://hal.archives-ouvertes.fr/hal-03224143
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Submitted on : Tuesday, May 11, 2021 - 4:12:41 PM
Last modification on : Wednesday, June 1, 2022 - 4:11:04 AM
Long-term archiving on: : Thursday, August 12, 2021 - 7:16:55 PM

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Maxime Sanselone, Stephane Sanchez, Cédric Sanza, David Panzoli, yves Duthen. Control of non player characters in a medical learning game with Monte Carlo Tree Search (poster). Annual Conference on Genetic and Evolutionary Computation COnference (GECCO 2014), SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation, Jul 2014, Vancouver, Canada. pp.51--52, ⟨10.1145/2598394.2598473⟩. ⟨hal-03224143⟩

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