Flexible and Purposeful NPC Behaviors using Real-Time Genetic Control

There is an increasing need in modern computer games for non-player characters (NPCs) with robust behaviors that achieve game objectives while appearing flexible and believable to the human players interacting with those NPCs. Evolutionary approaches to game artificial intelligence (game AI) have produced successful results for complex game winning strategies, realistic behavior patterns for groups of simulated entities, and more. However, there has been relatively little effort on evolutionary techniques for producing rich NPC behaviors for interaction with human players. To explore whether evolutionary mechanisms can support real-time control of NPCs to produce flexible and purposeful behavior, we present our initial efforts at integrating a genetic algorithm based robotic controller with an off-the-shelf game to control one or more NPCs dynamically. We describe the integration effort and our initial observations, and discuss our plan for achieving richer NPC control and for performing more detailed analysis of the behaviors of the resulting NPCs.

[1]  Sushil J. Louis,et al.  Playing to learn: case-injected genetic algorithms for learning to play computer games , 2005, IEEE Transactions on Evolutionary Computation.

[2]  Talib S. Hussain,et al.  Advocates and critics for tactical behaviors in UGV navigation , 2005, SPIE Defense + Commercial Sensing.

[3]  Moshe Sipper,et al.  Using GP-Gammon: Using Genetic Programming to Evolve Backgammon Players , 2005, EuroGP.

[4]  Risto Miikkulainen,et al.  Evolving Soccer Keepaway Players Through Task Decomposition , 2005, Machine Learning.

[5]  Talib S. Hussain,et al.  An Abstraction Framework for Cooperation Among Agents and People in a Virtual World , 2006, AIIDE.

[6]  Alexander Nareyek,et al.  Intelligent Agents for Computer Games , 2006 .

[7]  Risto Miikkulainen,et al.  Evolving Keepaway Soccer Players through Task Decomposition , 2003, GECCO.

[8]  Daniel Livingstone,et al.  Turing's test and believable AI in games , 2006, Comput. Entertain..

[9]  David M. Bourg,et al.  AI for Game Developers , 2004 .

[10]  Risto Miikkulainen,et al.  Real-time neuroevolution in the NERO video game , 2005, IEEE Transactions on Evolutionary Computation.

[11]  Risto Miikkulainen,et al.  Evolving robocup keepaway players through task decomposition , 2003 .

[12]  David B. Fogel,et al.  Evolving an expert checkers playing program without using human expertise , 2001, IEEE Trans. Evol. Comput..

[13]  David B. Fogel,et al.  Generating novel tactics through evolutionary computation , 1998, SGAR.

[14]  Barry G. Silverman,et al.  More Realistic Human Behavior Models for Agents in Virtual Worlds: Emotion, Stress, and Value Ontologies , 2001 .

[15]  Brian Magerko,et al.  AI Characters and Directors for Interactive Computer Games , 2004, AAAI.

[16]  Talib S. Hussain,et al.  Evolution-Based Deliberative Planning for Cooperating Unmanned Ground Vehicles in a Dynamic Environment , 2004, GECCO.

[17]  Clark C. Guest,et al.  Planning an endgame move set for the game RISK: a comparison of search algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[18]  John E. Laird,et al.  Intelligent Agents in Computer Games , 1999, AAAI/IAAI.

[19]  W ReynoldsCraig.  Flocks, herds and schools: A distributed behavioral model , 1987 .

[20]  James A. Hendler,et al.  Co-evolving Soccer Softbot Team Coordination with Genetic Programming , 1997, RoboCup.

[21]  Sushil J. Louis,et al.  Using a genetic algorithm to tune first-person shooter bots , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).