The problem of achieving competitive game play in a board game, against an intelligent opponent, is a well-known and studied field of Artificial Intelligence (AI). This area of research has seen major breakthroughs in recent years, particularly in the game of Go. However, popular hobby board games, and particularly Trading Card Games, have unique qualities that make them very challenging to existing game playing techniques, partly due to enormous branching factors. This remains a largely unexamined domain and is the arena we operate in. To attempt to tackle some of these daunting requirements, we introduce the novel concept of “Representative” Moves (RMs). Rather than examine the complete list of available moves at a given node, we rather propose the strategy of considering only a subset of moves that are determined to be representative of the player’s strategic options. We demonstrate that in the context of a simplified Trading Card Game, the use of RMs leads to a greatly improved search speed and an extremely limited branching factor. This permits the AI player to play more intelligently than the same algorithm that does not employ them.
IFIP Advances in Information and Communication Technology
School of Computer Science

Taucer, A.H. (Armando H.), Polk, S. (Spencer), & Oommen, J. (2018). On addressing the challenges of complex stochastic games using “representative” moves. In IFIP Advances in Information and Communication Technology. doi:10.1007/978-3-319-92007-8_1