Automatic generation of alternative starting positions for simple traditional board games Conference Paper


Author(s): Ahmed, Umair Z; Chatterjee, Krishnendu; Gulwani, Sumit
Title: Automatic generation of alternative starting positions for simple traditional board games
Title Series: Proceedings of the National Conference on Artificial Intelligence
Affiliation IST Austria
Abstract: Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role not only in the development of mathematical and logical skills, but also in the emotional and social development. In this paper, we address the problem of generating targeted starting positions for such games. This can facilitate new approaches for bringing novice players to mastery, and also leads to discovery of interesting game variants. We present an approach that generates starting states of varying hardness levels for player 1 in a two-player board game, given rules of the board game, the desired number of steps required for player 1 to win, and the expertise levels of the two players. Our approach leverages symbolic methods and iterative simulation to efficiently search the extremely large state space. We present experimental results that include discovery of states of varying hardness levels for several simple grid-based board games. The presence of such states for standard game variants like 4×4 Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased.
Keywords: Automatic Generation; Board games; Grid-based; Hardness levels; Iterative simulation; New approaches; Social development; Symbolic methods
Conference Title: IAAI: Innovative Applications of Artificial Intelligence
Volume: 2
Conference Dates: January 25-30, 2016
Conference Location: Austin, Texas, USA
ISBN: 978-157735700-1
Publisher: AAAI Press  
Date Published: 2015-01-01
Start Page: 745
End Page: 752
URL:
Notes: A Technical Report of this paper is available at: https://repository.ist.ac.at/id/eprint/146.
Open access: yes (repository)
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