Title :
Real-time challenge balance in an RTS game using rtNEAT
Author :
Olesen, Jacob Kaae ; Yannakakis, Georgios N. ; Hallam, John
Author_Institution :
Maersk McKinney Moller Inst., Univ. of Southern Denmark, Odense
Abstract :
This paper explores using the NEAT and rtNEAT neuro-evolution methodologies to generate intelligent opponents in real-time strategy (RTS) games. The main objective is to adapt the challenge generated by the game opponents to match the skill of a player in real-time, ultimately leading to a higher entertainment value perceived by a human player of the game. Results indicate the effectiveness of NEAT and rtNEAT but demonstrate their limitations for use in real-time strategy games.
Keywords :
computer games; evolutionary computation; learning (artificial intelligence); neural nets; real-time systems; topology; artificial neural network training; neuro-evolution of augmenting topology; real-time challenge balance; real-time strategy game; rtNEAT neuro-evolution methodology; Artificial intelligence; Artificial neural networks; Game theory; Genetic algorithms; Humans; Jacobian matrices; Learning systems; Machine learning; Psychology; Testing;
Conference_Titel :
Computational Intelligence and Games, 2008. CIG '08. IEEE Symposium On
Conference_Location :
Perth, WA
Print_ISBN :
978-1-4244-2973-8
Electronic_ISBN :
978-1-4244-2974-5
DOI :
10.1109/CIG.2008.5035625
