Evolving Story Narrative Using Surrogate Models of Human Judgement

  • Kun Wang
  • Vinh Bui
  • Eleni Petraki
  • Hussein A. Abbass
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 208)


Communication has been an active field of research in Robotics. However, less work has been done in the ability of robots to negotiate meanings of the world through storytelling. In this paper, we address this gap from the perspective of evolving stories. By approximating human evaluation of stories to guide the evolution, we can automate the story evolutionary process without interacting with humans. First, a multi-objective story evolution approach is applied where the approximated human story evaluation model automatically evaluates the subjective story metrics such as coherence, novelty and interestingness. We then use humans again to validate the stories narrated by the machine. Results show that for each of the human subjects, the stories collected after story evolution are regarded as better stories compared to the initial stories. Some interesting relationships are revealed and discussed in details.


evolving story narrative genotype-phenotype mapping objective story metrics human evaluation model 


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  1. 1.
    Gelin, R., d’Alessandro, C., Le, Q., Deroo, O., Doukhan, D., Martin, J., Pelachaud, C., Rilliard, A., Rosset, S.: Towards a storytelling humanoid robot. In: AAAI Fall Symposium Series (2010)Google Scholar
  2. 2.
    Hsueh-Min, C., Von-Wun, S.: Planning-based narrative generation in simulated game universes. IEEE Transactions on Computational Intelligence and AI in Games 1(3), 200–213 (2009)CrossRefGoogle Scholar
  3. 3.
    McKeever, W., Gilmour, D., Lehman, L., Stirtzinger, A., Krause, L.: Scenario management and automated scenario generation. In: Kevin, S., Alex, F.S. (eds.) Modeling and Simulation for Military Applications, Florida, vol. 6228, pp. 62281A.1–62281A.12. SPIE (2006)Google Scholar
  4. 4.
    Díaz-Agudo, B., Gervás, P., Peinado, F.: A case based reasoning approach to story plot generation. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 142–156. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Pérez, R., Sharples, M.: Mexica: A computer model of a cognitive account of creative writing. Journal of Experimental & Theoretical Artificial Intelligence 13(2), 119–139 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Bui, V., Abbbass, H., Bender, A.: Evolving stories: Grammar evolution for automatic plot generation. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)Google Scholar
  7. 7.
    Wang, K., Bui, V.Q., Abbass, H.A.: Evolving stories: Tree adjoining grammar guided genetic programming for complex plot generation. In: Deb, K., Bhattacharya, A., Chakraborti, N., Chakroborty, P., Das, S., Dutta, J., Gupta, S.K., Jain, A., Aggarwal, V., Branke, J., Louis, S.J., Tan, K.C. (eds.) SEAL 2010. LNCS, vol. 6457, pp. 135–145. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Kowaliw, T., Dorin, A., McCormack, J.: Promoting creative design in interactive evolutionary computation. IEEE Transactions on Evolutionary Computation (2011)Google Scholar
  9. 9.
    Deb, K., Sinha, A., Korhonen, P., Wallenius, J.: An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. IEEE Transactions on Evolutionary Computation 14(5), 723–739 (2010)CrossRefGoogle Scholar
  10. 10.
    Brintrup, A., Ramsden, J., Tiwari, A.: An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization. Computers in Industry 58(3), 279–291 (2007)CrossRefGoogle Scholar
  11. 11.
    Takagi, H.: Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation. Proceedings of the IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  12. 12.
    Babbar-Sebens, M., Minsker, B.: Interactive genetic algorithm with mixed initiative interaction for multi-criteria ground water monitoring design. Applied Soft Computing (2011)Google Scholar
  13. 13.
    Wang, K., Bui, V., Petraki, E., Abbass, H.: From subjective to objective metrics for evolutionary story narration using event permutations (2012)Google Scholar
  14. 14.
    Weisberg, S.: Applied linear regression, vol. 528. Wiley (2005)Google Scholar
  15. 15.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  16. 16.
    Michalewicz, Z., Fogel, D.: How to solve it: modern heuristics. Springer-Verlag New York Inc. (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kun Wang
    • 1
  • Vinh Bui
    • 1
  • Eleni Petraki
    • 2
  • Hussein A. Abbass
    • 1
  1. 1.School of Engineering and ITUNSWCanberraAustralia
  2. 2.Faculty of Arts and DesignUniversity of CanberraCanberraAustralia

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