Adversarial neural networks for playing hide-and-search board game Scotland Yard

This paper investigates the design of game playing agents, which should automatically play an asymmetric hide-and-search-based board game with imperfect information, called Scotland Yard. Neural network approaches have been developed to make the agents behave human-like in the sense that they would assess the game environment in a way a human would assess it. Specifically, a thorough investigation has been conducted on the application of adversarial neural network combined with Q-learning for designing the game playing agents in the game. The searchers, called detectives and the hider, called Mister X (Mr. X) have been modeled as neural network agents, which play the game of Scotland Yard. Though it is a type of two-player (or, two-sided) game, all the five detectives must cooperate to capture the hider to win the game. A special kind of feature space has been designed for both detectives and Mr. X that would aid the process of cooperation among the detectives. Rigorous experiments have been conducted, and the performance in each experiment has been noted. The evidence from the obtained results demonstrates that the designed neural agents could show promising performance in terms of learning the game, cooperating, and making efforts to win the game.