Abstract
Designs of champion-level systems dedicated to a game have been considered as milestones for Artificial Intelligence. Such a success has not yet happened for the game of Bridge because (i) Bridge is a partially observable game (ii) a Bridge player must be able to explain at some point the meaning of his actions to his opponents. This paper presents a simple supervised learning problem in Bridge: given a ‘limit hand’, should a player bid or not, only considering his hand and the context of his decision. We describe this problem and some of its candidate modelisations. We then experiment state of the art propositional machine learning and ILP systems on this problem. Results of these preliminary experiments show that ILP systems are competitive or even outperform propositional Machine Learning systems. ILP systems are moreover able to build explicit models that have been validated by expert Bridge players.
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This research work is conducted as part of the \(\nu \)Bridge project (former name AlphaBridge) supported by NukkAI Inc., Paris.
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An input variable already occurs in the left part of the clause under development, while an output variable does not occur in the current clause (and in its head) and is therefore existentially quantified.
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Legras, S., Rouveirol, C., Ventos, V. (2018). The Game of Bridge: A Challenge for ILP. In: Riguzzi, F., Bellodi, E., Zese, R. (eds) Inductive Logic Programming. ILP 2018. Lecture Notes in Computer Science(), vol 11105. Springer, Cham. https://doi.org/10.1007/978-3-319-99960-9_5
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