Artificial Intelligence Review

, Volume 27, Issue 4, pp 223–244 | Cite as

Evolving team behaviours in environments of varying difficulty

Article

Abstract

This paper investigates how varying the difficulty of the environment can affect the evolution of team behaviour in a combative game setting. The difficulty of the environment is altered by varying the perceptual capabilities of the agents in the game. The behaviours of the agents are evolved using a genetic program. These experiments show that the level of difficulty of the environment does have an impact on the evolvability of effective team behaviours; i.e. simpler environments are more conducive to the evolution of effective team behaviours than more difficult environments. In addition, the experiments show that no one best solution from any environment is optimal for all environments.

Keywords

Genetic programming Team behaviours Team evolution Shooter games 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bakkes S, Spronck P, Postma EO (2004) Team: the team-oriented evolutionary adaptability mechanism. In: Rauterberg M (ed) Proceedings of the third international conference on entertainment computing (ICEC 2004). Lecture notes in computer science, vol 3166. Springer, pp 273–282Google Scholar
  2. Buckland M (2005) Programming game AI by example. Wordware Publishing, Inc., Plano, TXGoogle Scholar
  3. Buckland M, Collins M (2002) AI techniques for game programming. Premier Press, Portland, ORGoogle Scholar
  4. Champandard AJ (2004) AI game development: synthetic creatures with learning and reactive behaviours. New Riders Publishing, Thousand Oaks, CAGoogle Scholar
  5. Cole N, Louis S, Miles C (2004) Using a genetic algorithm to tune first–person shooter bots. In: Congress on evolutionary computation 2004, vol 1, pp 139–145Google Scholar
  6. Doherty D, O’Riordan C (2006) Evolving tactical behaviours for teams of agents in single player action games. In: CGAMES 2006 9th international conference on computer games: AI, animation, mobile, educational & serious games, pp 121–126Google Scholar
  7. Doherty D, O’Riordan C (2007) A phenotypic analysis of gp-evolved team behaviours. In: GECCO ’07: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM Press, New York, NY, USA, pp 1951–1958Google Scholar
  8. Ehlis T (2000) Application of genetic programming to the “snake game”. GamedevNet 1(175), http://www.gamedev.net/reference/articles/article1175.asp. Accessed 9th October 2003
  9. Eskin E, Siegel E (1999) Genetic programming applied to othello: introducing students to machine learning research. In: SIGCSE ’99: The proceedings of the thirtieth SIGCSE technical symposium on computer science education. ACM Press, New York, NY, USA, pp 242–246Google Scholar
  10. Fogel DB (1993) Using evolutionary programming to create neural networks that are capable of playing tic-tac-toe. In: Proceedings of the American power conference, IEEE, pp 875–879Google Scholar
  11. Fogel DB (2002) Blondie24: playing at the edge of AI. Morgan Kaufmann Publishers Inc., San Francisco, CA, USAGoogle Scholar
  12. Frayn C (2005) An evolutionary approach to strategies for the game of monopoly. In: Proceedings of the 2005 IEEE symposium on computational intelligence and games (CIG05)Google Scholar
  13. GSC-GameWorld (2006) S.T.A.L.K.E.R.: Shadow of Chernobyl. http://www.stalker-game.com/en/
  14. Hauptman A, Sipper M (2005) Gp-endchess: using genetic programming to evolve chess endgame players. In: Proceedings of the 8th European conference on genetic programming, pp 120–131Google Scholar
  15. Haynes T, Sen S (1995) Evolving behavioral strategies in predators and prey. In: Sen S(eds) International joint conference on artificial intelligence-95 workshop on adaptation and learning in multiagent systems. Morgan Kaufmann, Montreal, Quebec, Canada, pp 32–37Google Scholar
  16. Haynes T, Sen S, Schoenefeld D, Wainwright R (1995) Evolving a team. In: Siegel EV, Koza JR(eds) Working notes for the AAAI symposium on genetic programming. AAAI, Cambridge, MAGoogle Scholar
  17. Haynes T, Wainwright R, Sen S (1995b) Evolving cooperation strategies. In: Lesser V (ed) Proceedings of the 1st international conference on multi-agent systems. MIT Press, San Francisco, CA, p 450, citeseer.ist.psu.edu/haynes94evolving.html
  18. ID-Software (1994) Doom. http://www.idsoftware.com/
  19. ID-Software (1996) Quake. http://www.idsoftware.com/
  20. Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Eshelman L (ed) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, San Francisco, CA, pp 184–192Google Scholar
  21. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USAMATHGoogle Scholar
  22. LaLena M (1997) Teamwork in genetic programming. Master’s thesis, Rochester Institute of Technology, School of Computer Science and TechnologyGoogle Scholar
  23. Lassabe N, Sanchez S, Luga H, Duthen Y (2006) Genetically programmed strategies for chess endgame. In: GECCO ’06: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM Press, New York, NY, USA, pp 831–838Google Scholar
  24. Lionhead-Studios (2001) Black & White. http://www.lionhead.com/bw/
  25. Luke S, Spector L (1996) Evolving teamwork and coordination with genetic programming. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic programming, 1996: Proceedings of the 1st annual conference. MIT Press, Stanford University, CA, USA pp 150–156Google Scholar
  26. Luke S, Hohn C, Farris J, Jackson G, Hendler J (1997) Co-evolving soccer softbot team coordination with genetic programming. In: International joint conference on artificial intelligence-97 first international workshop on RoboCup. Nagoya, JapanGoogle Scholar
  27. Poli R, Vanneschi L (2007) Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms. In: GECCO ’07: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM Press, New York, NY, USA, pp 1335–1342Google Scholar
  28. Ponsen M (2004) Improving adaptive game-AI with evolutionary learning. Master’s thesis, Delft University of TechnologyGoogle Scholar
  29. Raik S, Durnota B (1994) The evolution of sporting strategies. In: Stonier RJ, Yu XH(eds) Complex systems: mechanisms of adaption. IOS Press, Amsterdam, The Netherlands, pp 85–92Google Scholar
  30. Reynolds CW (1993) An evolved, vision-based behavioral model of coordinated group motion. In: Proceedings of the 2nd international conference on from animals to animats 2: simulation of adaptive behavior. MIT Press, Cambridge, MA, USA, pp 384–392Google Scholar
  31. Richards N, Moriarty D, McQuesten P, Miikkulainen R (1997) Evolving neural networks to play Go. In: Proceedings of the 7th international conference on genetic algorithms. East Lansing, MIGoogle Scholar
  32. Richards MD, Whitley D, Beveridge JR, Mytkowicz T, Nguyen D, Rome D (2005) Evolving cooperative strategies for uav teams. In: GECCO ’05: Proceedings of the 7th annual conference on genetic and evolutionary computation. ACM Press, New York, NY, USA, pp 1721–1728Google Scholar
  33. Rosin CD, Belew RK (1995) Methods for competitive co-evolution: finding opponents worth beating. In: Eshelman L(eds) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, San Francisco, CA, pp 373–380Google Scholar
  34. Stanley KO, Bryant BD, Miikkulainen R (2005) Evolving neural network agents in the nero video game. In: Proceedings of the IEEE 2005 symposium on computational intelligence and games (CIG05)Google Scholar
  35. Thurau C, Bauckhage C, Sagerer G (2004) Imitation learning at all levels of game–AI. In: Proceedings of the international conference on computer games, Artificial Intelligence, Design and Education, pp 402–408Google Scholar
  36. Valve (2000) Counter strike. http://www.counter-strike.com
  37. Vanneschi L, Clergue M, Collard P, Tomassini M, Verel S (2004) Fitness clouds and problem hardness in genetic programming. In: KD et al (ed) GECCO ’04: Proceedings of the 6th annual conference on genetic and evolutionary computation. Lecture notes in computer science, vol 3103. Springer-Verlag, Seattle, WA, USA, pp 690–701Google Scholar
  38. Vanneschi L, Tomassini M, Collard P, Vérel S (2006) Negative slope coefficient: a measure to characterize genetic programming fitness landscapes. In: Collet P, Tomassini M, Ebner M, Gustafson S, Ekárt A (eds) Proceedings of the 9th European conference on genetic programming. Lecture notes in computer science, vol 3905. Springer, pp 178–189Google Scholar
  39. Yannakakis GN, Hallam J (2004) Evolving opponents for interesting interactive computer games. In: Proceedings of the 8th international conference on simulation of adaptive behaviour (SAB04), pp 499–508Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Department of Information TechnologyNational University of IrelandGalwayIreland

Personalised recommendations