Real-time imitation-based adaptation of gaming behaviour in modern computer games

ABSTRACT

In the course of the recent complexification and sophistication of commercial computer games, the creation of competitive artificial players that are able to behave intelligently and successfully in the featured highly dynamic and complex virtual worlds has become a considerable challenge. This paper describes an evolutionary real-time adaptation approach to produce competitive artificial players in an action game. The proposed method is inspired by the idea of social learning or cultural evolution. Thus, the agents try to adapt to the level of their opponents by the exchange of information about advantageous behaviours within the population. In addition, the behaviour of the opponents and other players is recorded and used to create more sophisticated and human-like agents.

REFERENCES

• 1: S. J. Blackmore. The Meme Machine. Oxford University Press, 2000.
• 2: K. Chielens and F. Heylighen. Operationalization of Meme Selection Criteria: Methodologies to Empirically Test Memetic Predictions. In Proceedings of the Joint Symposium on Socially Inspired Computing (AISB'05), pages 14--20, 2005.
• 3: R. Conte and M. Paolucci. Intelligent Social Learning. Journal of Artificial Societies and Social Simulation, 4(1):U61--U82, 2001.
• 4: S. Priesterjahn. Online Adaptation and Imitation in Modern Computer Games. PhD thesis, University of Paderborn, 2008.
• 5: S. Priesterjahn, O. Kramer, A. Weimer, and A. Goebels. Evolution of Reactive Rules in Multi-Player Computer Games Based on Imitation. In Proceedings of the International Conference on Natural Computation (ICNC'06), volume 2, pages 744--755. Springer, 2005.
• 6: Steffen Priesterjahn , Alexander Weimer, An evolutionary online adaptation method for modern computer games based on imitation, Proceedings of the 9th annual conference on Genetic and evolutionary computation, July 07-11, 2007, London, England [doi>10.1145/1276958.1277031]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Intelligent agents

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Multiagent systems
      I.2.6 Learning
          Subjects: Knowledge acquisition

General Terms:
Algorithms

Keywords:
artificial intelligence, games, machine learning, multiagent systems, online algorithms