Trace-Based Multi- Cristeria Preselection Approach for Decision Making in Interactive Applications like Video Games

  • Hoang Nam Ho
  • Mourad Rabah
  • Samuel Nowakowski
  • Pascal Estraillier


The decision-making in games is essential to make them more automated and smart. A decision algorithm performs its calculations on the set of all the possible solutions. This increases the computation time and may become a combinatorial explosion problem if we have a huge solution space. To overcome this problem, we present our work on relevant solutions preselection before making a decision. We propose a two-steps strategy: i) the first step analyses the system’s traces (users past executions) to identify all the potential solutions; ii) the second step aims to estimate the relevance, called utility, of each of these potential solutions. We get a set of alternative solutions that can be used as an input to any decision algorithm. We illustrate our approach on the Tamagotchi game.


Interactive adaptive system Traces Prediction Utility Multi-criteria decisionmaking 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European journal of Operational research 200(1), 198-215.Google Scholar
  2. Bourg, D. M., & Seemann, G. (2004). AI for Game Developers. Sebastopol: O’Reilly Media, Inc.Google Scholar
  3. Brun, P., Beaudouin-Lafon, M. (1995). A taxonomy and evaluation of formalisms for the specification of interactive systems. In: M. A. R. Kirby, A. J. Dix & J. E. Finlay (Eds.), Proceedings of the HCI’95 conference. People and Computers X (pp. 197-212). Cambridge: Cambridge University Press.Google Scholar
  4. Burke, R. (2007). Hybrid Web Recommender Systems. In P. Brusilovsky, A. Kobsa & W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization (pp. 377-408). Berlin: Springer.Google Scholar
  5. Cheetham, W. (2003). Global Grade Selector: A Recommender System for Supporting the Sale of Plastic Resin. In K. D. Ashley & D. Bridge (Eds.), Proceedings of the 5th International Conference on Case-based Reasoning: Research and Development (pp. 96-106). Heidelberg: Springer.Google Scholar
  6. Cornuéjols, A., & Miclet, L. (2011). Apprentissage artificiel: concepts et algorithmes, Paris: Editions Eyrolles.Google Scholar
  7. Corrente, S., Greco, S., Słowiński, R. (2013). Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE. Omega 41(5), 820–846.Google Scholar
  8. Dang, K., Pham, P., Champagnat, R., & Rabah, M. (2013). Linear Logic Validation and Hierarchical Modeling for Interactive Storytelling Control. In D. Reidsma, H. Katayose & A. Nijholt (Eds.), Proceedings of the 10th International Conference, ACE 2013, Boekelo, The Netherlands, November 12-15, 2013. (pp. 524–527). Cham: Springer International Publishing.Google Scholar
  9. Dill, K., & Mark, D. (2010). Improving AI Decision Modeling Through Utility Theory (video content). URL: Last accessed: 28 May 2017.
  10. Dill, K., Mark, D. (2012). Embracing the Dark Art of Mathematical Modeling in AI (video content). URL: Last accessed: 27 May 2017.
  11. Domingos, P., Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine learning 29(2), 103-130.Google Scholar
  12. Doumat, R., Egyed-Zsigmond, E., Pinon, J.-M. (2010). User Trace-Based Recommendation System for a Digital Archive. In Bichindaritz, I. & Montani, S. (Eds.), Case-Based Reasoning. Research and Development. ICCBR 2010. Lecture Notes in Computer Science, Vol 6176 (pp. 360-374). Berlin: Springer.Google Scholar
  13. Evans, R. (2009). AI Challenges in Sims 3 klk – Invited talk in The Fifth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Conference, Last accessed: 28 May 2017.
  14. Guo, Y., Hu, J., Peng, Y. (2011). Research on CBR system based on data mining. Applied Soft Computing 11(8), 5006–5014.Google Scholar
  15. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA Data Mining Software: An Update. ACM SIGKDD explorations newsletter 11(1), 10-18.Google Scholar
  16. Hand, D. J., & Yu, K. (2001). Idiot’s Bayes – not so stupid after all? International statistical review 69(3), 385-398.Google Scholar
  17. Hanson, P., Rich, C. (2010). A Non-modal Approach to Integrating Dialogue and Action. In M. Youngblood & Vadim Bulitko (Eds.), Proceedings of the Sixth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (pp. 126–131). Standford: AAAI Press.Google Scholar
  18. Hatami-Marbini, A., Tavana, M.: An extension of the Electre I method for group decision-making under a fuzzy environment. Omega 41(5), 373-386.Google Scholar
  19. Ho, H. N. (2015). Décision multicritère à base de traces pour les applications interactives a exécution adaptative. URL: Last accessed: 28 May 2017.
  20. Ho, H. N., Rabah, M., Nowakowski, S., Estraillier, P. (2014). Trace-Based Weighting Approach for Multiple Criteria Decision Making. Journal of Software 9(8), 2180-2187.Google Scholar
  21. Ho, H. N., Rabah, M., Nowakowski, S., Estraillier, P. (2015). Application of Trace-Based Subjective Logic to User Preferences Modeling. In M. Davis, A. Fehnker, A. McIver & A. Voronokov (Eds.), 20th International Conference on Logic Programming, Artificial Intelligence and Reasoning (pp. 94-105). Berlin: Springer.Google Scholar
  22. Ho, H. N., Rabah, M., Nowakowski, S., Estraillier, P. (2016). Toward a Trace-Based PROMETHEE II Method to answer What can teachers do? In A. Micarelli, J. Stamper & K. Panourgia (Eds.), Online Distance Learning Applications. 13th International Conference on Intelligent Tutoring Systems. (pp. 480-484). Berlin: Springer.Google Scholar
  23. Karol, A., Nebel, B., Stanton, C., & Williams, M.-A. (2004). Case Based Game Play in the RoboCup Four-Legged League Part I. The Theoretical Model. In D. Polani, B. Browning, A. Bonarini & K. Yoshida (Eds.), RoboCup 2003: Robot Soccer World Cup VII. RoboCup 2003. Lecture Notes in Computer Science, Vol 3020 (pp. 739-747). Berlin: Springer.Google Scholar
  24. Laflaquière, J., Settouti, L. S., Prié, Y., Mille, A. (2006): Trace-Based Framework for Experience Management and Engineering. In B. Gabrys, R. J. Howlett & L. C. Jain (Eds.), Knowledge-Based Intelligent Information and Enginering Systems: 10th International Conference, KES 2006 (pp. 1171-1178). Dordrecht: Springer.Google Scholar
  25. Mark, D. (n.d.) Intrisic Algorithm – IA on AI. URL: Last accessed: 28 May 2017.
  26. Marling, C., Tomko, M., Gillen, M., Alex, D., & Chelberg, D. (2003). Case-based reasoning for planning and world modeling in the robocup small sized league. In U. Visser, P. Doherty, G. Lakemeyer & M. Veloso (Eds.), IJCAI Workshop on issues in designing physical agents for dynamic real-time environments (pp. 29-36). URL: Last accessed: 28 May 2017.
  27. Ontañón, S., & Ram, A. (2011). Case-Based Reasoning and User-Generated Artificial Intelligence for Real-Time Strategy Games. In M. Gonzalo, M. Gómez-Martín & M. Antonio (Eds.), Artificial Intelligence for Computer Games (pp. 103-124). New York: Springer.Google Scholar
  28. Parsons, S., Wooldridge, M. (2002). An Introduction to Game Theory and Decision Theory. In: Parsons, S., Gmytrasiewicz, P., & Wooldridge, M. (Eds.), Game Theory and Decision Theory in Agent-Based Systems (pp. 1-28). Boston: Springer Science & Business Media.Google Scholar
  29. Köksalan, M., Wallenius, J., Zionts, S. (2011). Multiple Criteria Decision Making: From Early History to the 21st Century. Signapore: World Scientific.Google Scholar
  30. Pham, P. T., Rabah, M., & Estraillier, P. (2015). A Situation-Based Multi-Agent Architecture for Handling Misunderstandings in Interaction. International Journal of Applied Mathematics and Computer Science 25(3), 439-454.Google Scholar
  31. Podinovski, V. V. (2014). Decision making under uncertainty with unknown utility function and rank-ordered probabilities. European Journal of Operational Research 239(2), 537-541.Google Scholar
  32. Riesbeck, C. K., & Schank, R. C. (2013). Inside Case-Based Reasoning. Milton Park: Taylor & Francis.Google Scholar
  33. Ros, R., Arcos, J. L., de Mantaras, R., Veloso, M. (2009). A Case-based Approach for Coordinated Action Selection in Robot Soccer. Artificial Intelligence 173(9-10), 1014-1039.Google Scholar
  34. Russell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Upper Saddle River: Pearson Education.Google Scholar
  35. Sánchez-Pelegrín, R., Gómez-Martín, M. A., & Díaz-Agudo, B. (2005). A CBR module for a strategy videogame. 1st Workshop on Computer Gaming and Simulation Environments, at 6th International Conference on Case-Based Reasoning (ICCBR). URL: Last accessed: 28 May 2017.
  36. Settouti, L.S., Prié, Y., Cram, D., Champin, P., Cnrs, U.M.R., Lyon, U.: A Trace-Based Framework for supporting Digital Object Memories. In Schneider et al. (Eds.), Proccedings of the 1st International Workshop on Digital Object Memories (DOMe’09) in the 5th International Conference on Intelligent Environments (IE 09), Barcelona (pp. 39-44). Amsterdam: IOS Press.Google Scholar
  37. Sutton, R. S., & Barto, A.G. (2012). Introduction to reinforcement learning. Cambridge. MIT Press.Google Scholar
  38. Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Upper Saddle River. Pearson Education.Google Scholar
  39. Taillandier, P., & Stinckwich, S. (2011). Using the PROMETHEE multi-criteria decision making method to define new exploration strategies for rescue robots. In Proceedings of the EEE International Workshop on Safety, Security, and Rescue Robotics, Kyoto, Japan. doi:  10.1109/SSRR.2011.6106747.
  40. Triantaphyllou, E., Shu, B., Sanchez, S. N., Ray, T. (1998). Multi-Criteria Decision Making: An Operations Research Approach. Encyclopedia of electrical and electronics engineering 15, 175-186.Google Scholar
  41. Vapnik, V. (2000). The Nature of Statistical Learning Theory. New York: Springer.Google Scholar
  42. Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine learning 8(3-4), 279-292.Google Scholar
  43. Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 Algorithms in Data Mining. Knowledge and information systems 14(1), 1-37.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2018

Authors and Affiliations

  • Hoang Nam Ho
    • 1
  • Mourad Rabah
    • 1
  • Samuel Nowakowski
    • 2
  • Pascal Estraillier
    • 1
  1. 1.Computer Science DepartmentUniversity of La RochelleLa RochelleFrance
  2. 2.Faculty of Sciences and TechnologiesUniversity of LorraineVandœuvre-lès-NancyFrance

Personalised recommendations