Developing a General Video Game AI Controller Based on an Evolutionary Approach

  • Kristiyan BalabanovEmail author
  • Doina Logofătu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)


The field of general intelligence is one, where humans can still easily outperform machines. In the context of our work we describe it as the ability to learn an activity, like playing a game, without any prior knowledge of goals and rules. The agent has to learn by doing/playing and examining the consequences of its actions. Many traditional techniques in reinforcement learning, such as SARSA and Q-Learning, can provide a good solution to this category of problems. In our paper, however, we propose an alternative method based on evolutionary algorithms to overcome the extensive computing for all state-action pairs needed in traditional approaches. We have evaluated various parent selection algorithms and two different fitness functions. “The General Video Game AI Competition” (GVGAI), where contestants submit a playing agent programmed with some learning algorithm to be tested against unknown games, has been used as a benchmark for the performance of our implementation.


Artificial intelligence General intelligence Planning agents Video game controllers 


  1. 1.
    Back, T.: Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, vol. 1, pp. 57–62, June 1994.
  2. 2.
    Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic Algorithms and Their Application, pp. 14–21. L. Erlbaum Associates Inc., Hillsdale (1987).
  3. 3.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series, 2nd edn. Springer, Heidelberg (2015). Scholar
  4. 4.
    Gerdes, I., Klawonn, F., Kruse, R.: Evolutionäre Algorithmen: genetische Algorithmen - Strategien und Optimierungsverfahren - Beispielanwendungen. Computational intelligence, Wiesbaden, 1. aufl. edn. (2004).
  5. 5.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)CrossRefGoogle Scholar
  6. 6.
    Michalewicz, Z.: Genetic algorithms + data structures: = evolution programs: with 36 tables. Berlin [u.a.], 3, rev. and extended ed. edn. (1996).
  7. 7.
    Ronald, S.: Duplicate genotypes in a genetic algorithm. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, Cat. No. 98TH8360, pp. 793–798, May 1998.
  8. 8.
    Schaul, T.: A video game description language for model-based or interactive learning. In: 2013 IEEE Conference on Computational Intelligence in Games, CIG, pp. 1–8, August 2013.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringFrankfurt University of Applied SciencesFrankfurt am MainGermany

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