Artificial Intelligence for the Card Game Durak

Abstract

Card games with imperfect information present a unique challenge for many common game-playing algorithms because of their hidden game state. The objective of this thesis is to create a framework for implementing and testing various AI agents in the popular imperfect information card game "Durak" to identify the most effective approach in this environment. This paper presents a theoretical and experimental comparison of agents using various techniques, including rules-based heuristics, minimax search, and Monte Carlo tree search. In our analysis, we found that the Monte Carlo Tree Search agent performed the best among the implemented AI agents, whereas the rule-based heuristic agent and the minimax agent were less effective. 1