Evolving Artificial General Intelligence for Video Game Controllers

Part of the Genetic and Evolutionary Computation book series (GEVO)


The General Video Game Playing Competition (GVGAI) defines a challenge of creating controllers for general video game playing, a testbed—as it were—for examining the issue of artificial general intelligence. We develop herein a game controller that mimics human learning behavior, focusing on the ability to generalize from experience and diminish learning time as new games present themselves. We use genetic programming to evolve hyper-heuristic-based general players. Our results show the effectiveness of evolution in meeting the generality challenge.


Genetic programming Hyper-heuristics Video games GVG-AI competition 


  1. 1.
    Arfaee, S.J., Zilles, S., Holte, R.C.: Bootstrap learning of heuristic functions. In: Proceedings of the 3rd International Symposium on Combinatorial Search (SoCS2010), pp. 52–59 (2010)Google Scholar
  2. 2.
    Browne, C.B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A survey of monte carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4(1), 1–43 (2012)CrossRefGoogle Scholar
  3. 3.
    Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyperheuristic for timetabling and rostering. J. Heuristics 9(6), 451–470 (2003). CrossRefGoogle Scholar
  4. 4.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J. (eds.) Handbook of Meta-Heuristics, 2nd edn., pp. 449–468. Springer, Boston (2010)Google Scholar
  5. 5.
    Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. IEEE Trans. Evol. Comput. 14(6), 942–958 (2010). CrossRefGoogle Scholar
  6. 6.
    Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: Automating the packing heuristic design process with genetic programming. Evol. Comput. 20(1), 63–89 (2012). CrossRefGoogle Scholar
  7. 7.
    Cowling, P.I., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E.K., Erben, W. (eds.) PATAT 2000: Practice and Theory of Automated Timetabling III. Lecture Notes in Computer Science, vol. 2079, pp. 176–190. Springer, Berlin (2000). Google Scholar
  8. 8.
    Elyasaf, A., Zaritsky, Y., Hauptman, A., Sipper, M.: Evolving solvers for FreeCell and the sliding-tile puzzle. In: Borrajo, D., Likhachev, M., López, C.L. (eds.) Proceedings of the Fourth Annual Symposium on Combinatorial Search, SoCS 2011, Castell de Cardona, Barcelona, Spain, July 15–16, 2011. AAAI Press, Palo Alto (2011). Google Scholar
  9. 9.
    Elyasaf, A., Hauptman, A., Sipper, M.: Evolutionary design of FreeCell solvers. IEEE Trans. Comput. Intell. AI Games 4(4), 270–281 (2012). CrossRefGoogle Scholar
  10. 10.
    Elyasaf, A., Vaks, P., Milo, N., Sipper, M., Ziv-Ukelson, M.: Learning heuristics for mining RNA sequence-structure motifs. In: Genetic Programming Theory and Practice XIII (GPTP 2015). Springer, Cham (2015)Google Scholar
  11. 11.
    Fawcett, C., Karpas, E., Helmert, M., Roger, G., Hoos, H.: Fd-autotune: domain-specific configuration using fast-downward. In: Proceedings of ICAPS-PAL 2011 (2011)Google Scholar
  12. 12.
    Hauptman, A., Elyasaf, A., Sipper, M., Karmon, A.: GP-rush: using genetic programming to evolve solvers for the Rush Hour puzzle. In: GECCO’09: Proceedings of 11th Annual Conference on Genetic and Evolutionary Computation Conference, pp. 955–962. ACM, New York (2009).
  13. 13.
    Jones, J.: Abstract syntax tree implementation idioms. In: Proceedings of the 10th Conference on Pattern Languages of Programs (plop2003), pp. 1–10 (2003)Google Scholar
  14. 14.
    Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1), 99–134 (1998)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Levine, J., Westerberg, H., Galea, M., Humphreys, D.: Evolutionary-based learning of generalised policies for AI planning domains. In: Rothlauf, F. (ed.) Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009), pp. 1195–1202. ACM, New York (2009)Google Scholar
  16. 16.
    Park, H., Kim, K.J.: MCTS with influence map for general video game playing. In: IEEE Conference on Computational Intelligence and Games (CIG), 2015, pp. 534–535. IEEE, Piscataway (2015)Google Scholar
  17. 17.
    Perez, D., Samothrakis, S., Lucas, S.: Knowledge-based fast evolutionary MCTS for general video game playing. In: IEEE Conference on Computational Intelligence and Games (CIG), 2014, pp. 1–8. IEEE, Piscataway (2014)Google Scholar
  18. 18.
    Perez, D., Samothrakis, S., Togelius, J., Schaul, T., Lucas, S., Couëtoux, A., Lee, J., Lim, C.U., Thompson, T.: The 2014 general video game playing competition. IEEE Trans. Comput. Intell. AI Games 8, 229–243 (2015)CrossRefGoogle Scholar
  19. 19.
    Pohl, I.: Heuristic search viewed as path finding in a graph. Artif. Intell. 1(3), 193–204 (1970)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Samadi, M., Felner, A., Schaeffer, J.: Learning from multiple heuristics. In: Fox, D., Gomes, C.P. (eds.) Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008), pp. 357–362. AAAI Press, Palo Alto (2008)Google Scholar
  21. 21.
    Yoon, S.W., Fern, A., Givan, R.: Learning control knowledge for forward search planning. J. Mach. Learn. Res. 9, 683–718 (2008). MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceBen-Gurion UniversityBeer-ShevaIsrael

Personalised recommendations