ASP with Applications to Mazes and Levels

  • Mark J. NelsonEmail author
  • Adam M. Smith
Part of the Computational Synthesis and Creative Systems book series (CSACS)


Answer set programming (ASP) is an approach to logic programming, where constraints and logical relations are declared in a Prolog-like language. ASP solvers can be used to find world configurations that satisfy constraints expressed in this language. Interestingly, many forms of content generation can be formulated as constraint-solving problems, and thus expressed in ASP. For example, maps can be represented as the position of all objects in the map, and the space of permissible maps limited by constraints expressed in the language. This chapter discusses how to use ASP for generating different types of mazes, using generation of dungeons as a running example.


Design Space Integrity Constraint Choice Rule Game Mechanic Valid Path 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baral, C.: Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press (2003)Google Scholar
  2. 2.
    Boenn, G., Brain, M., De Vos, M., ffitch, J.: Automatic music composition using answer set programming. Theory and Practice of Logic Programming 11(2–3), 397–427 (2011)Google Scholar
  3. 3.
    Butler, E., Smith, A.M., Liu, Y.E., Popovic, Z.: A mixed-initiative tool for designing level progressions in games. In: Proceedings of the 26th ACM Symposium on User Interface Software and Technology, pp. 377–386 (2013)Google Scholar
  4. 4.
    Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Morgan and Claypool (2012)Google Scholar
  5. 5.
    Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.: Potassco: The Potsdam answer set solving collection. AI Communications 24(2), 107–124 (2011)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Horswill, I.D., Foged, L.: Fast procedural level population with playability constraints. In: Proceedings of the Eighth Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 20–25 (2012)Google Scholar
  7. 7.
    Nelson, M.J., Mateas, M.: Recombinable game mechanics for automated design support. In: Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 84–89 (2008)Google Scholar
  8. 8.
    Smith, A.M., Butler, E., Popović, Z.: Quantifying over play: Constraining undesirable solutions in puzzle design. In: Proceedings of the Eighth International Conference on the Foundations of Digital Games, pp. 221–228 (2013)Google Scholar
  9. 9.
    Smith, A.M., Mateas, M.: Answer set programming for procedural content generation: A design space approach. IEEE Transactions on Computational Intelligence and AI in Games 3(3), 187–200 (2011)CrossRefGoogle Scholar
  10. 10.
    Smith, A.M., Nelson, M.J., Mateas, M.: Computational support for play testing game sketches. In: Proceedings of the Fifth Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 167–172 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The MetaMakers InstituteFalmouth UniversityPenrynUK
  2. 2.Department of Computational MediaUniversity of California Santa CruzSanta CruzUSA

Personalised recommendations