Abstract
The recent trends in the gaming world have been more inclined to shooter games which covers a wide range of audiences including streamers and many more people. People expect the games to be closer to reality for a lively experience. Behaviors of non-playable characters (NPC) in various games like Grand Theft Auto are studied and various methods of defining behaviors to non-playable characters like behavior trees and Q-Learning behavior tree are compared for performance and activities. The comparisons of the resultant agents are made using certain performance criteria like the closeness of AI behavior to the humans, latency to respond to events etc.,
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
https://store.steampowered.com/app/730/CounterStrike_Global_Offensive/
Annandale D (2006) The subversive carnival of grand theft auto: San Andreas. In: The meaning and culture of grand theft auto: critical essays, pp 88–103
Aranda G, Botti V, Carrascosa C (2009) Mmog based on mas: the mmog layer. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems, vol 2
Voulgari I, Komis V (2011) On studying collaborative learning interactions in massively multiplayer online games. In: 2011 Third international conference on games and virtual worlds for serious applications. IEEE
Buchan A, Taylor J (2016) A qualitative exploration of factors affecting group cohesion and team play in multiplayer online battle arenas (mobas). Comput Games J 5(1):65–89
Kruse J, Connor AM, Marks S (2021) An interactive multi-agent system for game design. Comput Games J 10(1):41–63
Dawe M et al (2013) Behavior selection algorithms. In: Game AI pro: collected wisdom of game AI professionals 47
Isla D (2005) Handling complexity in the Halo 2 AI. In: Game developers conference, vol 12
Ogren P (2012) Increasing modularity of UAV control systems using computer game behavior trees. In: AIAA guidance, navigation, and control conference 2012
Fu Y, Qin L, Yin Q (2016) A reinforcement learning behavior tree framework for game AI. In: 2016 International conference on economics, social science, arts, education and management engineering. Atlantis Press
Dahlan F (2013) The study of slang in pedestrian dialogue of GTA San Andreas video game. Abstract of Undergraduate Research, Faculty of Humanities, Bung Hatta University 3(2)
Dey R, Child C (2013) Ql-bt: enhancing behaviour tree design and implementation with q-learning. In: 2013 IEEE conference on computational intelligence in games (CIG). IEEE
Du X, Li Q, Han J (2009) Applying hierarchical reinforcement learning to computer games. In: 2009 IEEE international conference on automation and logistics. IEEE
Sutton RS (1998) Introduction to reinforcement learning. MIT Press, Cambridge, Mass
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saranya Rubini, S., Ram, R.V., Narasiman, C.V., Umar, J.M., Naveen, S. (2023). Behaviors of Modern Game Non-playable Characters. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_27
Download citation
DOI: https://doi.org/10.1007/978-981-19-7753-4_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7752-7
Online ISBN: 978-981-19-7753-4
eBook Packages: EngineeringEngineering (R0)