Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games

Authors

  • Umair Ahmed Indian Institute of Technology Kanpur
  • Krishnendu Chatterjee The Institute of Science and Technology
  • Sumit Gulwani Microsoft Research, Redmond

DOI:

https://doi.org/10.1609/aaai.v29i1.9287

Keywords:

Board Games, Games on Graphs, Symbolic methods and Binary Decision Diagrams (BDDs), Problem generation for games

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 x 4 Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased.

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Published

2015-02-10

How to Cite

Ahmed, U., Chatterjee, K., & Gulwani, S. (2015). Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9287

Issue

Section

AAAI Technical Track: Game Playing and Interactive Entertainment