Investigating combinations of machine learning and classification techniques in a game environment

Edmundson, Ruth, Danby, Richard, Brotherton, Kris, Livingstone, Emma and Allcock, Lee (2016) Investigating combinations of machine learning and classification techniques in a game environment. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, Piscataway, pp. 1306-1311. ISBN 978-1-5090-4094-0

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/FSKD.2016.7603367

Abstract

An open world turn based monster battle game was developed in Java using the popular LibGDX game framework applying multiple machine learning algorithms for its mechanics consisting of an ID3 decision tree, perceptron, naïve Bayes classifier and A* pathfinding in an attempt to imitate `machine intelligence'. A tiled map was used as the game area containing multiple AI agents with different personalities that change depending on the difficulty level chosen. The aim of the game focuses on the player defeating each `intelligent machine' non-player character's (NPC) upon interaction with each other, when player and enemy NPC sprites meet a battle screen appears to allow the player and enemy to engage in a turn-based battle with their monsters. When a battle is lost the player loses a life, otherwise they can approach and engage other enemy agents to battle on the map, and thus the game is called `Battle Monsters'.

Item Type: Book Section
Uncontrolled Keywords: java, A∗ pathfinding algorithms, ID3 decision tree, neural network, naïve Bayes classifier, collision detection, game development, ensemble methods, hybrid methods, machine learning, libGDX
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 09 Dec 2016 13:57
Last Modified: 31 Jan 2017 11:14
URI: http://nrl.northumbria.ac.uk/id/eprint/28844

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