Level of Detail Event Generation

  • Luis Flores
  • David ThueEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10690)


Level of detail is a method that involves optimizing the amount of detail that is simulated for some entity. We introduce an event generation method to optimize the level of detail of upcoming events in a simulation. Our method implements a cognitive model, which uses an estimate of the player’s knowledge to estimate their interest in different aspects of the world. Our method predicts the salience of upcoming events, and uses this salience value to define the level of detail of potential new events. An evaluation of our method’s predictive capacity shows generally higher accuracy than a baseline predictor.



Some parts of this text appear in the first author’s M.Sc. dissertation [4].


  1. 1.
    Cardona-Rivera, R.E., Cassell, B.A., Ware, S.G., Young, R.M.: Indexter: a computational model of the event-indexing situation model for characterizing narratives. In: Proceedings of the 3rd Workshop on Computational Models of Narrative, pp. 34–43 (2012)Google Scholar
  2. 2.
    Compton, K., Mateas, M.: Casual creators. In: Proceedings of the International Conference on Computational Creativity, ICCC (2015)Google Scholar
  3. 3.
    Cournoyer, F., Fortier, A.: Massive crowd on assassin’s creed unity: AI recycling. In: Presentation at the Game Developer’s Conference (GDC 2015). GDC Vault, UBM Tech. (2015)Google Scholar
  4. 4.
    Flores, L.: Level of detail event generation. M.Sc. dissertation. School of Computer Science, Reykjavik University (2017)Google Scholar
  5. 5.
    Magerko, B.: Evaluating preemptive story direction in the interactive drama architecture. J. Game Dev. 2(3), 25–52 (2007)Google Scholar
  6. 6.
    Mihalcea, R., Tarau, P.: Textrank: bringing order into texts. In: Conference on Empirical Methods in Natural Language Processing, pp. 404–411. Association for Computational Linguistics (2004)Google Scholar
  7. 7.
    Paris, S., Gerdelan, A., O’Sullivan, C.: CA-LOD: collision avoidance level of detail for scalable, controllable crowds. In: Egges, A., Geraerts, R., Overmars, M. (eds.) MIG 2009. LNCS, vol. 5884, pp. 13–28. Springer, Heidelberg (2009). CrossRefGoogle Scholar
  8. 8.
    Roth, M., Ben-David, A., Deutscher, D., Flysher, G., Horn, I., Leichtberg, A., Leiser, N., Matias, Y., Merom, R.: Suggesting friends using the implicit social graph. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 233–242. ACM (2010)Google Scholar
  9. 9.
    Rowe, J.P., Lester, J.C.: Modeling user knowledge with dynamic Bayesian networks in interactive narrative environments. In: 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 57–62. AAAI Press (2010)Google Scholar
  10. 10.
    Sunshine-Hill, B.: Perceptually driven simulation. Ph.D. thesis, University of Pennsylvania (2011)Google Scholar
  11. 11.
    Sunshine-Hill, B.: Managing simulation level-of-detail with the LOD trader. In: 6th International Conference on Motion in Games, pp. 13–18. ACM (2013)Google Scholar
  12. 12.
    Zwaan, R.A., Langston, M.C., Graesser, A.C.: The construction of situation models in narrative comprehension: an event-indexing model. Psychol. Sci. 6(5), 292–297 (1995). JSTORCrossRefGoogle Scholar
  13. 13.
    Zwaan, R.A., Radvansky, G.A.: Situation models in language comprehension and memory. Psychol. Bull. 123(2), 162–185 (1998). APACrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Analysis and Design of Intelligent Agents, School of Computer ScienceReykjavik UniversityReykjavikIceland

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