Exploring learning techniques based on decision trees and their performance in platform games
Metadatos
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comunitat-uji-handle2:10234/71324
comunitat-uji-handle3:10234/169451
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Título
Exploring learning techniques based on decision trees and their performance in platform gamesAutoría
Tutor/Supervisor; Universidad.Departamento
Sanz Valero, Pedro José; Universitat Jaume I. Departament d'Enginyeria i Ciència dels ComputadorsFecha de publicación
2020-07-09Editor
Universitat Jaume IResumen
This document presents the Final Degree Work of the Bachelor’s Degree in Video Game
Design and Development. The work consists of the study and implementation of machine
learning techniques based on decision trees. ... [+]
This document presents the Final Degree Work of the Bachelor’s Degree in Video Game
Design and Development. The work consists of the study and implementation of machine
learning techniques based on decision trees. The focus is set on Quinlan’s Inductive
Decision Tree algorithm (ID3) and its extension, the Incremental Decision Tree learning
algorithm (ID4).
The learning methods are applied to the classic Super Mario Bros. The artificial
intelligence agents are implemented and trained within the Mario AI Framework . This is a
framework for using AI methods with a version of Super Mario Bros. The framework includes
features such as level generators, observation grid, and already implemented playing
agents.
In order to demonstrate the reliability and feasibility of the system, some tests have been
carried out as an experimental validation. These preliminary results showcase the pros and
cons of the applied learning approach and open the door to continue exploring learning
techniques in other videogame contexts. [-]
Palabras clave / Materias
Descripción
Treball final de Grau en Disseny i Desenvolupament de Videojocs. Codi: VJ1241. Curs acadèmic: 2019/2020
Tipo de documento
info:eu-repo/semantics/bachelorThesisDerechos de acceso
info:eu-repo/semantics/openAccess
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