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Answer Set Programming for Declarative Content Specification: A Scalable Partitioning-Based Approach

  • Francesco Calimeri
  • Stefano Germano
  • Giovambattista Ianni
  • Francesco PacenzaEmail author
  • Armando Pezzimenti
  • Andrea Tucci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)

Abstract

Procedural Content Generation is applied in the development process of many commercial games: automatically generated game contents are delivered to players in order to offer a constantly changing user experience and enrich the game itself. Usually, the generative process relies on search-based non-deterministic algorithms, which encode one or more techniques for guaranteeing “legal” yet diversified output. Declarative approaches to content generation, more properly defined as Declarative Content Specification techniques, like the ones based on Answer Set Programming, allow to focus on describing content requirements rather than programming ad-hoc generation engines, and to fast prototype generation techniques themselves. This work investigates to what extent ASP-based DCS is scalable enough for industrial contexts, by proposing a partitioning-based approach. A working prototype, available as an Unity Asset and as a GVGAI framework level generator is presented.

Keywords

Answer Set Programming Procedural content generation Game content generation Artificial intelligence in games Computational intelligence in games Declarative Content Specification 

References

  1. 1.
    Abiteboul, S., Hull, R., Vianu, V. (eds.): Foundations of Databases: The Logical Level. Addison-Wesley Longman Publishing Co., Inc., Boston (1995)Google Scholar
  2. 2.
    Calì, A., Gottlob, G., Lukasiewicz, T.: Tractable query answering over ontologies with datalog+/\(-\). In: Description Logics, CEUR Workshop Proceedings, vol. 477. CEUR-WS.org (2009)Google Scholar
  3. 3.
    Calimeri, F., Gebser, M., Maratea, M., Ricca, F.: Design and results of the fifth answer set programming competition. Artif. Intell. 231, 151–181 (2016).  https://doi.org/10.1016/j.artint.2015.09.008MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Eiter, T., Ianni, G., Krennwallner, T.: Answer set programming: a primer. In: Tessaris, S., et al. (eds.) Reasoning Web 2009. LNCS, vol. 5689, pp. 40–110. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03754-2_2CrossRefGoogle Scholar
  5. 5.
    Erdem, E., Gelfond, M., Leone, N.: Applications of answer set programming. AI Mag. 37(3), 53–68 (2016)CrossRefGoogle Scholar
  6. 6.
    Faber, W., Leone, N., Ricca, F.: Answer set programming. In: Wah, B.W. (ed.) Wiley Encyclopedia of Computer Science and Engineering. Wiley (2008)  https://doi.org/10.1002/9780470050118.ecse226
  7. 7.
    Fuscà, D., Germano, S., Zangari, J., Anastasio, M., Calimeri, F., Perri, S.: A framework for easing the development of applications embedding answer set programming. In: PPDP, pp. 38–49. ACM (2016)Google Scholar
  8. 8.
    Gebser, M., Maratea, M., Ricca, F.: The sixth answer set programming competition. J. Artif. Intell. Res. 60, 41–95 (2017).  https://doi.org/10.1613/jair.5373MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9(3/4), 365–386 (1991)CrossRefGoogle Scholar
  10. 10.
    Kaufmann, B., Leone, N., Perri, S., Schaub, T.: Grounding and solving in answer set programming. AI Mag. 37(3), 25–32 (2016)CrossRefGoogle Scholar
  11. 11.
    Leone, N., Ricca, F.: Answer set programming: a tour from the basics to advanced development tools and industrial applications. In: Faber, W., Paschke, A. (eds.) Reasoning Web 2015. LNCS, vol. 9203, pp. 308–326. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21768-0_10CrossRefGoogle Scholar
  12. 12.
    Liebana, D.P., Samothrakis, S., Togelius, J., Schaul, T., Lucas, S.M.: General video game AI: competition, challenges and opportunities. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 12–17 February 2016, Phoenix, Arizona, USA, pp. 4335–4337 (2016). http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11853
  13. 13.
    Lierler, Y., Maratea, M., Ricca, F.: Systems, engineering environments, and competitions. AI Mag. 37(3), 45–52 (2016)CrossRefGoogle Scholar
  14. 14.
    Lifschitz, V.: Answer set planning. In: ICLP, pp. 23–37. MIT Press (1999)Google Scholar
  15. 15.
    Nelson, M.J., Smith, A.M.: ASP with applications to mazes and levels. In: Shaker, N., Togelius, J., Nelson, M.J. (eds.) Procedural Content Generation in Games, pp. 143–157. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42716-4_8CrossRefGoogle Scholar
  16. 16.
    Nielsen, T.S., Barros, G.A.B., Togelius, J., Nelson, M.J.: Towards generating arcade game rules with VGDL. In: 2015 IEEE Conference on Computational Intelligence and Games (CIG), pp. 185–192, August 2015.  https://doi.org/10.1109/CIG.2015.7317941
  17. 17.
    Ortiz, M.: Ontology based query answering: the story so far. In: Proceedings of AMW (2013). http://ceur-ws.org/Vol-1087/keynote3.pdf
  18. 18.
    Pérez-Liébana, D., Liu, J., Khalifa, A., Gaina, R.D., Togelius, J., Lucas, S.M.: General video game AI: a multi-track framework for evaluating agents, games and content generation algorithms. CoRR abs/1802.10363 (2018). http://arxiv.org/abs/1802.10363
  19. 19.
    Shaker, N., Liapis, A., Togelius, J., Lopes, R., Bidarra, R.: Constructive generation methods for dungeons and levels. In: Shaker, N., Togelius, M., Nelson, M.J. (eds.) Procedural Content Generation in Games. CSCS, pp. 31–55. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42716-4_3CrossRefGoogle Scholar
  20. 20.
    Shaker, N., Togelius, J., Nelson, M.J. (eds.): Procedural Content Generation in Games. CSACS. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42716-4CrossRefGoogle Scholar
  21. 21.
    Smith, A.M., Mateas, M.: Answer set programming for procedural content generation: a design space approach. IEEE Trans. Comput. Intell. AI in Games 3(3), 187–200 (2011).  https://doi.org/10.1109/TCIAIG.2011.2158545CrossRefGoogle Scholar
  22. 22.
    Togelius, J., Kastbjerg, E., Schedl, D., Yannakakis, G.N.: What is procedural content generation?: Mario on the borderline. In: Proceedings of the 2Nd International Workshop on Procedural Content Generation in Games, PCGames 2011, pp. 3:1–3:6. ACM, New York (2011).  https://doi.org/10.1145/2000919.2000922

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CalabriaRendeItaly

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