Using Simulation to Evaluate Data-Driven Agents

  • Elizabeth Sklar
  • Ilknur Icke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5269)


We use simulation to evaluate agents derived from humans interacting in a structured on-line environment. The data set was gathered from student users of an adaptive educational assessment. These data illustrate human behavior patterns within the environment, and we employed these data to train agents to emulate these patterns. The goal is to provide a technique for deriving a set of agents from such data, where individual agents emulate particular characteristics of separable groups of human users and the set of agents collectively represents the whole. The work presented here focuses on finding separable groups of human users according to their behavior patterns, and agents are trained to embody the group’s behavior. The burden of creating a meaningful training set is shared across a number of users instead of relying on a single user to produce enough data to train an agent. This methodology also effectively smooths out spurious behavior patterns found in individual humans and single performances, resulting in an agent that is a reliable representative of the group’s collective behavior. Our demonstrated approach takes data from hundreds of students, learns appropriate groupings of these students and produces agents which we evaluate in a simulated environment. We present details and results of these processes.


Behavior Pattern Cluster Member Human User Separable Group Educational Assessment 
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.


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  1. 1.
    VanLehn, K., Ohlsson, S., Nason, R.: Applications of simulated students: An exploration. Journal of Artificial Intelligence in Education 5(2), 135–175 (1996)Google Scholar
  2. 2.
    Sklar, E., Davies, M.: Multiagent simulation of learning environments. In: Fourth International Conference on Autonomous Agents and Multi Agent Systems (AAMAS) (2005)Google Scholar
  3. 3.
    Spoelstra, M., Sklar, E.: Using simulation to model and understand group learning. Agent Based Systems for Human Learning, International Transactions on Systems Science and Applications 4(1) (2008)Google Scholar
  4. 4.
    Sklar, E.: CEL: A Framework for Enabling an Internet Learning Community. PhD thesis, Department of Computer Science, Brandeis University (2000)Google Scholar
  5. 5.
    Cypher, A.: Eager: Programming repetitive tasks by example. In: Proceedings of CHI 1991 (1991)Google Scholar
  6. 6.
    Maes, P.: Agents that reduce work and information overload. Communications of the ACM 37(7), 31–40, 146 (1994)CrossRefGoogle Scholar
  7. 7.
    Balabanović, M.: Learning to Surf: Multiagent Systems for Adaptive Web Page Recomendation. PhD thesis, Stanford University (1998)Google Scholar
  8. 8.
    Sklar, E., Blair, A.D., Pollack, J.B.: Training Intelligent Agents Using Human Data Collected on the Internet. In: Agent Engineering, ch. 8, pp. 201–226. World Scientific, Singapore (2001)Google Scholar
  9. 9.
    Hofmann, K.: Subsymbolic user modeling in adaptive hypermedia. In: The 12th International Conference on Artificial Intelligence in Education, Young Researcher Track Proceedings, pp. 63–68 (2005)Google Scholar
  10. 10.
    Mavrikis, M.: Logging, replaying and analysing students’ interactions in a web-based ILE to improve student modeling. In: The 12th International Conference on Artificial Intelligence in Education, Young Researcher Track, pp. 101–106 (2005)Google Scholar
  11. 11.
    Merceron, A., Yacef, K.: Educational data mining: a case study. In: The 12th International Conference on Artificial Intelligence in Education (2005)Google Scholar
  12. 12.
  13. 13.
    Jacobs, N., Blockeel, H.: User modeling with sequential data. In: Proceedings of the 10th International Conference on HCI, pp. 557–561 (2003)Google Scholar
  14. 14.
    Basalto, N., Bellotti, R., De Carlo, F., Facchi, P., Pascazio, S.: Hausdorff clustering of financial time series. Physica A 379, 635–644 (2007)CrossRefGoogle Scholar
  15. 15.
    Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: ICPR (3), pp. 1135–1138 (2006)Google Scholar
  16. 16.
    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  17. 17.
    Russell, S., Norvig, P.: Artificial intelligence: A modern approach, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Elizabeth Sklar
    • 1
    • 2
  • Ilknur Icke
    • 2
  1. 1.Dept of Computer and Information Science, Brooklyn CollegeCity University of New YorkUSA
  2. 2.Dept of Computer Science, The Graduate CenterCity University of New YorkNew YorkUSA

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