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Planning in the midst of chaos: how a stochastic Blood Bowl model can help to identify key planning features

Abstract : For several decades now, games have become an important research ground for artificial intelligence. In addition to often present useful and complex problems, they also provide a clear framework thanks to their rules, sometimes numerous. In this article, we explore a very difficult two-players board game named Blood Bowl. This game allows the players to perform many different actions, which depend for a large part on the result of one or more dice rolls. Thus, it can be seen as a multi-action probabilistic problem driven by a Markov decision process. In this article, we present the first stochastic model of the main phase of Blood Bowl to our knowledge and the premise of a dedicated planning framework. Such a framework could offer interesting grounds and insights for modeling high turn-wise branch factor games.
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https://hal.archives-ouvertes.fr/hal-03345360
Contributor : Alexis Lebis Connect in order to contact the contributor
Submitted on : Thursday, September 16, 2021 - 11:47:18 AM
Last modification on : Thursday, March 10, 2022 - 2:32:56 PM
Long-term archiving on: : Friday, December 17, 2021 - 6:03:49 PM

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  • HAL Id : hal-03345360, version 1

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Jérémie Humeau, Alexis Lebis, Mathieu Vermeulen, Guillaume Lozenguez. Planning in the midst of chaos: how a stochastic Blood Bowl model can help to identify key planning features. IEEE Conference on Games, Aug 2021, Copenhagen (virtual), Denmark. ⟨hal-03345360⟩

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