Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/91519
Title: AGAPA : adaptive game artificial intelligence using a player-centered approach
Authors: Iavarone, Stefano (2013)
Keywords: Artificial intelligence
Computer games
Video games
Issue Date: 2013
Citation: Iavarone, S. (2013). AGAPA : adaptive game artificial intelligence using a player-centered approach (Bachelor's dissertation).
Abstract: In today's digital world, the video gaming industry is one of the most growing industries. A video game has many aspects, including the graphics, the engine, and player abilities. One of the aspects that has not had much improvement over the years is the game artificial intelligence (Al), which controls computer agents and the game environment. This project focuses on developing an Al solution that adapts to each player to increase diversity and player satisfaction. The project involves choosing a game to be used as a test-base for the design, implementation and testing of an adaptive Al. The Al should be able to learn to perform better against several opponents on a range of maps. An add-on is also created so that if turned on, the Al tries to balance the game after learning has completed. This balancing is the concept of difficulty scaling, one of the possible solutions for the Al to increase player satisfaction. The Al is built with a range of tactical behaviours and it learns to use the best behaviours over a period of learning trials during different situations. Research provided useful information about the different types of adaptive algorithms, and Q.- learning was chosen to be implemented as part of the Al in the Al Sandbox simulation game. With its functionalities in a stochastic SNP environment, AGAPA should be able to learn to adapt, diversify its behaviour and balance gameplay to simulate the possibilities of adaptive game Als in these types of games.
Description: B.SC.ICT(HONS)ARTIFICIAL INTELLIGENCE
URI: https://www.um.edu.mt/library/oar/handle/123456789/91519
Appears in Collections:Dissertations - FacICT - 2013
Dissertations - FacICTAI - 2002-2014

Files in This Item:
File Description SizeFormat 
B.SC.(HONS)ICT_Iavarone_Stefano_2013.PDF
  Restricted Access
11.06 MBAdobe PDFView/Open Request a copy


Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.