Skip to Main content Skip to Navigation
Conference papers

Monte-Carlo Tree Search for the Game of "7 Wonders"

Abstract : Monte-Carlo Tree Search algorithm, and in particular with the Upper Confidence Bounds formula, provided huge improvements for AI in numerous games, particularly in Go, Hex, Havannah, Amazons and Breakthrough. In this work we study this algorithm on a more complex game, the game of " 7 Wonders ". This card game gathers together several known difficult features, such as hidden information, N-player and stochasticity. It also includes an inter-player trading system that induces a combinatorial search to decide which decisions are legal. Moreover it is difficult to hand-craft an efficient evaluation function since the card values are heavily dependent upon the stage of the game and upon the other player decisions. We show that, in spite of the fact that " 7 Wonders " is apparently not so related to abstract games, a lot of known results still hold.
Document type :
Conference papers
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-01406496
Contributor : Fabien Teytaud <>
Submitted on : Thursday, December 1, 2016 - 11:35:25 AM
Last modification on : Tuesday, January 5, 2021 - 1:04:02 PM
Long-term archiving on: : Tuesday, March 21, 2017 - 6:39:35 AM

File

7wonders.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Denis Robilliard, Cyril Fonlupt, Fabien Teytaud. Monte-Carlo Tree Search for the Game of "7 Wonders". European Conference in Artificial Intelligence (ECAI), Aug 2014, Prague, Czech Republic. pp.64 - 77, ⟨10.1007/978-3-319-14923-3_5⟩. ⟨hal-01406496⟩

Share

Metrics

Record views

430

Files downloads

1376