Argumentative AI Director Using Defeasible Logic Programming

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 614)


In this work we present a novel implementation of an AI Director that uses argumentation techniques to decide dynamic adaptations in the level generation of a roguelike game called HermitArg. The architecture of the game introduces smart items with defeasible information to be analyzed in a dialectical process.


Strict Rule Dialectical Process Defeasible Logic Defeasible Rule Argumentation Line 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artif. Intell. 128(1–2), 203–235 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Booth, M.: The AI systems of Left 4 Dead. In: Keynote, 5th AIIDE (2009)Google Scholar
  3. 3.
  4. 4.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–358 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    García, A.J., Simari, G.R.: Defeasible logic programming: an argumentative approach. Theor. Pract. Logic Program. 4(1–2), 95–138 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Harrison, B.E., Roberts, D.L.: Analytics-driven dynamic game adaption for player retention in a 2-dimensional adventure game. In: 10th AIIDE, pp. 23–29 (2014)Google Scholar
  7. 7.
    Jennings-Teats, M., Smith, G., Wardrip-Fruin, N.: Polymorph: dynamic difficulty adjustment through level generation. In: Proceedings of 2010 Workshop on Procedural Content Generation in Games, pp. 1–14 (2010)Google Scholar
  8. 8.
    Kazmi, S., Palmer, I.J.: Action recognition for support of adaptive gameplay: a case study of a first person shooter. Int. J. Comp. Games Tech. 2010, 1–14 (2010)CrossRefGoogle Scholar
  9. 9.
    Lifschitz, V.: Foundations of logic programs. In: Brewka, G. (ed.) Principles of Knowledge Representation, pp. 69–128. CSLI Pub. (1996)Google Scholar
  10. 10.
    Lopes, R., Bidarra, R.: Adaptivity challenges in games and simulations: a survey. IEEE Trans. Comput. Intell. AI Games 3(2), 85–99 (2011)CrossRefGoogle Scholar
  11. 11.
    Magerko, B.: Evaluating preemptive story direction in the interactive drama architecture. J. Game Dev. 2(3), 25–52 (2005)Google Scholar
  12. 12.
    Rahwan, I., Simari, G.R.: Argumentation in Artificial Intelligence. Springer, New York (2009)Google Scholar
  13. 13.
    Riedl, M.O., Bulitko, V.: Interactive narrative: an intelligent systems approach. AI Mag. 34(1), 67–77 (2013)Google Scholar
  14. 14.
    Talbot, C.: Creating an artificially intelligent director (aid) for theatre and virtual environments. In: 12th AAMAS, pp. 1457–1458 (2013)Google Scholar
  15. 15.
    Thimm, M.: Tweety: A comprehensive collection of java libraries for logical aspects of artificial intelligence and knowledge representation. In: 14th International Conference on Principles of Knowledge Representation and Reasoning, pp. 528–537 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Artificial Intelligence Research and Development Laboratory (LIDIA), Department of Computer Science and Engineering (DCIC)Universidad Nacional del Sur (UNS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Bahía BlancaArgentina

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