Can Deep Networks Learn to Play by the Rules? A Case Study on Nine Men's Morris

Abstract

Deep networks have been successfully applied to a wide range of tasks in artificial intelligence, and game playing is certainly not an exception. In this paper, we present an experimental study to assess whether purely sub-symbolic systems, such as deep networks, are capable of learning to play by the rules, without any a-priori knowledge neither of the game, nor of its rules, but only by observing the matches played by another player. Similar problems arise in many other application domains, where the goal is to learn rules, policies, behaviours, or decisions, simply by the observation of the dynamics of a system. We present a case study conducted with residual networks on the popular board game of Nine Men's Morris, showing that this kind of sub-symbolic architecture is capable of correctly discriminating legal from illegal decisions, just from the observation of past matches of a single player.

 

Publication available at

https://doi.org/10.1109/TG.2018.2804039

 

Cite as

F. Chesani, A. Galassi, M. Lippi and P. Mello, "Can Deep Networks Learn to Play by the Rules? A Case Study on Nine Men's Morris," in IEEE Transactions on Games, vol. 10, no. 4, pp. 344-353, Dec. 2018. DOI: 10.1109/TG.2018.2804039

 

Additional material

Code repository: https://github.com/AGalassi/NNMM