Advances in Games Technology: Software, Models, and Intelligence

Edmond Prakash, Geoff Brindle, Kevin Jones, Suiping Zhou, Narendra S. Chaudhari and Kok-Wai Wong
Additional contact information
Edmond Prakash: Manchester Metropolitan University, Manchester, UK, e.prakash@mmu.ac.uk
Geoff Brindle: Manchester Metropolitan University, Manchester, UK, g.brindle@mmu.ac.uk
Kevin Jones: Nanyang Technological University, Singapore
Suiping Zhou: Nanyang Technological University, Singapore
Narendra S. Chaudhari: Nanyang Technological University, Singapore, asnarendra@ntu.edu.sg
Kok-Wai Wong: Murdoch University, Murdoch, Western Australia, Australia

Simulation & Gaming, 2009, vol. 40, issue 6, 752-801

Abstract: Games technology has undergone tremendous development. In this article, the authors report the rapid advancement that has been observed in the way games software is being developed, as well as in the development of games content using game engines. One area that has gained special attention is modeling the game environment such as terrain and buildings. This article presents the continuous level of detail terrain modeling techniques that can help generate and render realistic terrain in real time. Deployment of characters in the environment is increasingly common. This requires strategies to map scalable behavior characteristics for characters as well. The authors present two important aspects of crowd simulation: the realism of the crowd behavior and the computational overhead involved. A good simulation of crowd behavior requires delicate balance between these aspects. The focus in this article is on human behavior representation for crowd simulation. To enhance the player experience, the authors present the concept of player adaptive entertainment computing, which provides a personalized experience for each individual when interacting with the game. The current state of game development involves using very small percentage (typically 4% to 12%) of CPU time for game artificial intelligence (AI). Future game AI requires developing computational strategies that have little involvement of CPU for online play, while using CPU’s idle capacity when the game is not being played, thereby emphasizing the construction of complex game AI models offline. A framework of such nonconventional game AI models is introduced.

Keywords: artificial intelligence; behavior modeling; board game; character modeling; conversational avatars; environment modeling; game AI; game development; game editor; game engine; game software; gardening game; genetic algorithms; hybrid soft computing; memetic algorithms; neural network optimization; programmable tournament; racing game; small world models; terrain modeling (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:sae:simgam:v:40:y:2009:i:6:p:752-801

DOI: 10.1177/1046878109335120

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