Towards Solving Decision Making Problems Using Probabilistic Model Checking

Abstract

Decision making seeks the optimal choice for maximum rewards or minimal costs under certain conditions, requirements and constraints. Decision making problems in practice are usually complicated as they may be partially observable, stochastic, and dynamic. Such complexities make the traditional decision making methods like mathematical programming difficult to find the optimal choices effectively and efficiently. In this work, we conduct a case study with the 4-player Kuhn Poker game by combining machine learning with probabilistic model checking to generate optimal decisions. Experimental results show that the agent employing our method outperforms the conservative and bluffing players regardless of the positions of players.