Capitalizing on the Opponent's Uncertainty in Reconnaissance Blind Chess

Games have long served as an effective method for developing and testing artificial intelligence (AI) models designed to function in a complex and possibly adversarial environment. While computers have already outperformed humans in practically all traditional board games, such as checkers, chess or Go, as well as various games with imperfect information, e.g. Poker or Bridge, certain imperfect information games, especially those devised for the purpose of testing particular capabilities of AI agents, remain a challenge. An example of such a game is Reconnaissance Blind Chess (RBC) - a variant of chess in which players do not have full access to the information defining the current board state. In this paper, we present Zubat, an AI RBC playing agent. The agent harnesses the strength of the Stockfish chess engine and enriches it with auxiliary modules. Firstly, we propose to estimate the oppnnent's uncertainty with a recurrent neural network in order to prefer positions that maximize the opponent's uncertainty, thus impeding their selection of optimal moves. Secondly, we develop the risk-taker module that identifies high-reward moves that could potentially exploit the opponent's uncertainty. The mediator module is constructed to select a move to be played by combining all three pieces of available information (the Stockfish assessment of moves, the expected opponent's uncertainty level, potential gains from risk-taking moves). Experiments conducted in the publicly available RBC match-making system show high competitiveness of the proposed agent, which, at the time of writing, was ranked 2nd out of 117 bots and human players, on the RBC leaderboard.