Abstract
In this paper we introduce a project developed in Unity 5 which aims to aid in the rapid development and prototyping of games-based experiments. We present the Modular Games Benchmark an openly released tool-kit for games research. Using our framework, experiments can be created quickly without the need for extensive, customised or boilerplate code, aiding in reproducibility and standardisation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yannakakis, G.N.: Game AI revisited. In: Proceedings of the 9th conference on Computing Frontiers, pp. 285–292. ACM (2012)
Karakovskiy, S., Togelius, J.: The mario AI benchmark and competitions. IEEE Trans. Comput. Intell. AI Games 4(1), 55–67 (2012)
Perez, D., Nicolau, M., O’Neill, M., Brabazon, A.: Evolving behaviour trees for the mario AI competition using grammatical evolution. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6624, pp. 123–132. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20525-5_13
Togelius, J., Karakovskiy, S., Baumgarten, R.: The 2009 mario AI competition. In: IEEE Congress on Evolutionary Computation (CEC) 2010, pp. 1–8. IEEE (2010)
He, S., Wang, Y., Xie, F., Meng, J., Chen, H., Luo, S., Liu, Z., Zhu, Q.: Game player strategy pattern recognition and how UCT algorithms apply pre-knowledge of player’s strategy to improve opponent AI. In: International Conference on Computational Intelligence for Modelling Control & Automation 2008, pp. 1177–1181. IEEE (2008)
Yannakakis, G.N., Levine, J., Hallam, J.: An evolutionary approach for interactive computer games. In: Congress on Evolutionary Computation 2004, CEC2004, vol. 1, pp. 986–993. IEEE (2004)
Kalyanpur, A., Simon, M.: Pacman using genetic algorithms and neural networks. University of Maryland (2001)
Gallagher, M., Ryan, A.: Learning to play Pac-Man: an evolutionary, rule-based approach. In: The 2003 Congress on Evolutionary Computation 2003, CEC 2003, vol. 4, pp. 2462–2469. IEEE (2003)
Szita, I., Lörincz, A.: Learning to play using low-complexity rule-based policies: illustrations through Ms. Pac-Man. J. Artif. Intell. Res. (JAIR) 30, 659–684 (2007)
Galván-López, E., Swafford, J.M., O’Neill, M., Brabazon, A.: Evolving a Ms. PacMan controller using grammatical evolution. In: Di Chio, C., et al. (eds.) EvoApplications 2010. LNCS, vol. 6024, pp. 161–170. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12239-2_17
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Williams, B., Jackson, J., Hall, J., Headleand, C.J. (2018). Modular Games AI Benchmark. In: Lewis, P., Headleand, C., Battle, S., Ritsos, P. (eds) Artificial Life and Intelligent Agents. ALIA 2016. Communications in Computer and Information Science, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-319-90418-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-90418-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-90417-7
Online ISBN: 978-3-319-90418-4
eBook Packages: Computer ScienceComputer Science (R0)