Optimizing Propositional Networks

  • Chiara F. SironiEmail author
  • Mark H. M. Winands
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 705)


General Game Playing (GGP) programs need a Game Description Language (GDL) reasoner to be able to interpret the game rules and search for the best actions to play in the game. One method for interpreting the game rules consists of translating the GDL game description into an alternative representation that the player can use to reason more efficiently on the game. The Propositional Network (PropNet) is an example of such method. The use of PropNets in GGP has become popular due to the fact that PropNets can speed up the reasoning process by several orders of magnitude compared to custom-made or Prolog-based GDL reasoners, improving the quality of the search for the best actions. This paper analyzes the performance of a PropNet-based reasoner and evaluates four different optimizations for the PropNet structure that can help further increase its reasoning speed in terms of visited game states per second.



This work is funded by the Netherlands Organisation for Scientific Research (NWO) in the framework of the project GoGeneral, grant number 612.001.121.


  1. 1.
    Björnsson, Y., Finnsson, H.: CadiaPlayer: a simulation-based general game player. IEEE Trans. Comput. Intell. AI Games 1(1), 4–15 (2009)CrossRefGoogle Scholar
  2. 2.
    Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M. (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-75538-8_7 CrossRefGoogle Scholar
  3. 3.
    Cox, E., Schkufza, E., Madsen, R., Genesereth, M.R.: Factoring general games using propositional automata. In: Björnsson, Y., Stone, P., Thielscher, M. (eds.) Proceedings of the IJCAI Workshop on General Intelligence in Game-Playing Agents (GIGA), pp. 13–20 (2009)Google Scholar
  4. 4.
    Draper, S., Rose, A.: Sancho GGP player (2014).
  5. 5.
    Emslie, R.: Galvanise (2015).
  6. 6.
    Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.R.: General game playing: game description language specification. Technical report, Stanford University, Stanford, CA, USA (2008)Google Scholar
  7. 7.
    Schiffel, S., Björnsson, Y.: Efficiency of GDL reasoners. IEEE Trans. Comput. Intell. AI Games 6(4), 343–354 (2014)CrossRefGoogle Scholar
  8. 8.
    Schkufza, E., Love, N., Genesereth, M.: Propositional automata and cell automata: representational frameworks for discrete dynamic systems. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 56–66. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-89378-3_6 CrossRefGoogle Scholar
  9. 9.
    Schreiber, S.: The general game playing base package (2013).
  10. 10.
    Schreiber, S.: Games - base repository (2016).
  11. 11.
    Sturtevant, N.R.: An analysis of UCT in multi-player games. ICGA J. 31(4), 195–208 (2008)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Games and AI Group, Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands

Personalised recommendations