Sistema de recomendação de heróis para jogos MOBA utilizando aprendizado de máquina

Abstract

MOBA games are currently one of the most popular online game genres. In their basic gameplay, two teams of multiple players compete against each other to destroy the enemy’s base, controlling a powerful unit known as “hero”. Each hero has different abilities, roles and strengths. Thus, choosing a good combination of heroes is fundamental for the success in the game. In this dissertation we propose a recommendation system for selecting heroes in a MOBA game. We develop a mechanism based on association rules that suggests the most suitable heroes for composing a team, using data collected from a large number of Dota 2 matches. For evaluating the efficacy of the line-up, we trained a neural network capable of predicting the winner team with a 90.89% accuracy. The results of the recommendation system were very satisfactory with up to 76.4% success rate.