Level of Detail based AI Adaptation for Agents in Video Games

This paper suggests multi-agent systems (MASs) for implementing game artificial intelligence (AI) for video games. One of main hindrances against using MASs technology in video games has been the real-time con- straints for frame rendering. In order to deal with the real-time constraints, we introduce an adaptation-oriented approach for maintaining frame rate in acceptable ranges. The adaptation approach is inspired from the level of detail (LoD) technique in 3D graphics. We introduce agent organizations for defining different roles of agents in game AI. The computational requirements of agent roles have been prioritized according to their functional roles in a game. In this way, adapting computational requirements of game AI works as a means for maintaining frame rate in acceptable ranges. The proposed approach has been evaluated through a pilot experiment by using a proof of concept game. The pilot experiment shows that LoD based adaptation allows maintaining frame rate in acceptable ranges and therefore enhancing the quality of service.

[1]  Frank Dignum.  Agents for games and simulations , 2011, Autonomous Agents and Multi-Agent Systems.

[2]  Elisabeth André,et al.  Level of Detail Based Behavior Control for Virtual Characters , 2010, IVA.

[3]  T. Gendler.  Alief and Belief , 2008, Contemporary Epistemology.

[4]  Ian Millington,et al.  Artificial Intelligence for Games , 2006, The Morgan Kaufmann series in interactive 3D technology.

[5]  Daniel E Epner,et al.  Black and White , 2013, Annals of Internal Medicine.

[6]  Markus H. Gross,et al.  Level-of-detail for cognitive real-time characters , 2005, The Visual Computer.

[7]  Markus Gross,et al.  Towards a game agent , 2003 .

[8]  Jung Hong Chuang.  Level of Detail for 3D Graphics , 2002 .

[9]  Elisabeth André,et al.  Level of Detail AI for Virtual Characters in Games and Simulation , 2010, MIG.

[10]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[11]  Stephen McGlinchey,et al.  Biologically Inspired Artificial Intelligence for Computer Games , 2007 .

[12]  Michael F. Deering.  Data complexity for virtual reality: where do all the triangles go? , 1993, Proceedings of IEEE Virtual Reality Annual International Symposium.

[13]  Patrick Dickinson,et al.  Improving games AI performance using grouped hierarchical level of detail , 2010 .

[14]  Jeffrey M. Bradshaw,et al.  Agents for Games and Simulations: Trends in Techniques, Concepts and Design , 2009 .

[15]  Frank Dignum,et al.  Games and Agents: Designing Intelligent Gameplay , 2009, Int. J. Comput. Games Technol..

[16]  Carlos Delgado-Mata,et al.  AI opponents with personality traits in Überpong , 2008, INTETAIN '08.

[17]  Frank Dignum.  Agents for Games and Simulations II - Trends in Techniques, Concepts and Design [AGS 2010, The Second International Workshop on Agents for Games and Simulations, May 10, 2010, Toronto, Canada] , 2011, AGS.

[18]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[19]  Jacques Ferber,et al.  From Agents to Organizations: An Organizational View of Multi-agent Systems , 2003, AOSE.

[20]  Christoph Beat Niederberger,et al.  Behavior modeling and real-time simulation for autonomous agents using hierarchies and level-of-detail , 2005 .

[21]  James H. Clark,et al.  Hierarchical geometric models for visible surface algorithms , 1976, CACM.

[22]  Juliane Junker.  Agents for Games and Simulations, Trends in Techniques, Concepts and Design [AGS 2009, The First International Workshop on Agents for Games and Simulations, May 11, 2009, Budapest, Hungary] , 2009, AGS.

[23]  Steve Rabin,et al.  AI Game Programming Wisdom , 2002 .