Solving a Goal-planning task in the MASH project

Jean-Baptiste Hoock 1, 2 Jacques Bibai 2, 3
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : The MASH project is a collaborative platform with the aim to experiment different methods in an unknown environment of large size. The application is a goal-planning task in a 3D video game where runs are expensive. Moreover, there is no prior knowledge, the decisions have unknown semantics, observations on the environment are partial and of big size and accomplishing the task by taking random decisions always requires a very long run. So, solving this task is a big challenge. In this paper, we extend Monte-Carlo Tree Search, which has been proved very effective for applications in which simulating is easy and fast, to contexts in which there are only "real" expensive runs. This generic approach combines Clustering and Monte-Carlo Tree Search.
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Communication dans un congrès
The 2012 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2012), Nov 2012, Tainan, Taiwan. 2012
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Soumis le : mercredi 24 octobre 2012 - 14:02:14
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14
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  • HAL Id : hal-00738073, version 1

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Jean-Baptiste Hoock, Jacques Bibai. Solving a Goal-planning task in the MASH project. The 2012 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2012), Nov 2012, Tainan, Taiwan. 2012. 〈hal-00738073〉

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