A decision making framework for game playing using evolutionary optimization and learning

We introduce a decision making framework that uses evolutionary and learning methods. It is applied to competitive games to learn online the current opponent strategy and to adapt the system counter-strategy appropriately. We compared our system for the iterated prisoner's dilemma and rock-paper-scissors with three other methods against different typical game strategies as opponents. Results show that our system performs best in most cases and is able to adapt its strategy online to the current opponent. Moreover we could show that a good prediction of the opponent is no guaranty for a good payoff, since a good prediction is often the result of a poor opponent strategy which leads to a low payoff for both players.