Analysis of Algorithms for AI Virtual Player in the Production Management Training Platform

Abstract The article presents research focused on design, testing and evaluation of the Artificial Intelligence (AI) algorithms dedicated for Virtual Player (VP/bot) embedded in the Production Management Training Platform (TP). The innovative software enables complex teaching process in form of didactic games. The purpose of the study is to eliminate limitations of the prototype VP’s algorithmic structure which was primarily implemented in the Platform. The study comprise measurements aimed at comparing effectiveness and efficiency of the existing solution with the capabilities of the newly developed VP’s logic. The overview of the algorithmic concepts available for VPs in serious games was made indicating elements useful for the purpose of the analysed system. Considering drawbacks of the original bot’s engine architecture, the upgraded version was elaborated and tested in the simplified version of the game. The results of the verification show to prove dominance of the AI solution with Machine Learning over the existing hybrid approach. The training, evaluation and recruitment games offered by the Platform could successfully fill in the gap identified in the new paradigm of education for Industry 4.0. It assures teaching of multidiscipline knowledge as well as practical skills training in the competitive environment altogether with thorough evaluation procedure. The appropriate VP’s logic increases flexibility of the didactic game and enables to customize the learning process which is crucial for the successful education process aided with gaming tools.