Predictive systems: The game rock-paper-scissors as an example

Abstract

In simple-decision-making scenarios such as in repeated two-person games human behavior is to some extend predictable. To investigate this research question, we focused on developing a system for the Rock-Paper-Scissor (RPS) game. Our approach included three steps: (i) To generate a large data-base of experimental data, (ii) to analyze the data to detect systematic patterns and deviations from rational behavior within the test persons, and (iii) to employ methods from machine learning to identify patterns and predict the next throw of the opponent. We identified as the best current approach a Gated-Reccurent-Unit using User Statistics, which is able to predict the next throw and hence win in about 50% of the cases, beating state-of-the-art approaches. Potentials and limitations of our approach are discussed.