Intelligent Management of Data Driven Simulations to Support Model Building in the Social Sciences

  • Catriona Kennedy
  • Georgios Theodoropoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)

Abstract

Artificial intelligence (AI) can contribute to the management of a data driven simulation system, in particular with regard to adaptive selection of data and refinement of the model on which the simulation is based. We consider two different classes of intelligent agent that can control a data driven simulation: (a) an autonomous agent using internal simulation to test and refine a model of its environment and (b) an assistant agent managing a data-driven simulation to help humans understand a complex system (assisted model-building). In the first case the agent is situated in its environment and can use its own sensors to explore the data sources. In the second case, the agent has much less independent access to data and may have limited capability to refine the model on which the simulation is based. This is particularly true if the data contains subjective statements about the human view of the world, such as in the social sciences.

For complex systems involving human actors, we propose an architecture in which assistant agents cooperate with autonomous agents to build a more complete and reliable picture of the observed system.

Keywords

agent cognition decision support fault-tolerance simulation social sciences 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Darema, F.: Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems. Proceedings of the IEEE: Special Issue on Grid Computing 93, 692–697 (2005)Google Scholar
  2. 2.
    Low, M.Y.H., Lye, K.W., Lendermann, P., Turner, S.J., Chim, R.T.W., Leo, S.H.: An Agent-based Approach for Managing Symbiotic Simulation of Semiconductor Assembly and Test Operation. In: Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2005), Utrecht, The Netherlands (2005)Google Scholar
  3. 3.
    Plale, B., Gannon, D., Reed, D., Graves, S., Droegemeier, K., Wilhelmson, B., Ramamurthy, M.: Towards Dynamically Adaptive Weather Analysis and Forecasting in LEAD. In: Workshop on Dynamic Data Driven Application Systems at the International Conference on Computational Science (ICCS 2005), Atlanta, USA (2005)Google Scholar
  4. 4.
    Patrikalakis, N., McCarthy, J., Robinson, A., Schmidt, H., Evangelinos, C., Haley, P., Lalis, S., Lermustaux, P., Tian, R., Leslie, W., Cho, W.: Towards a dynamic data driven system for rapid adaptive interdisciplinary ocean forecasting. In: Darema, F. (ed.) Dynamic Data-Driven Application Systems. Kluwer Academic Publishers, Amsterdam (2004)Google Scholar
  5. 5.
    Birkin, M., Dew, P., Macfarland, O., Hodrien, J.: HYDRA: A prototype grid-enabled spatial decision support system. In: First International Conference on e-Social Science, Manchester, UK (2005)Google Scholar
  6. 6.
    Chaturvedi, R., Filatyev, S., Gore, J., Mellema, A.A.: Integrating Fire, Structure and Agent Models. In: Workshop on Dynamic Data Driven Application Systems at the International Conference on Computational Science (ICCS 2005), Atlanta, USA (2005)Google Scholar
  7. 7.
    Rich, E., Knight, K.: Artificial Intelligence. McGraw-Hill Higher Education (1990)Google Scholar
  8. 8.
    Harnad, S.: The symbol grounding problem. Physica D 42, 335–346 (1990)CrossRefGoogle Scholar
  9. 9.
    Edmonds, B., Moss, S.: From KISS to KIDS - an anti-simplistic modelling approach. In: Joint Workshop on Multi-Agent and Multi-Agent-Based Simulation (MAMABS 2004) at the 3rd Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2004). Columbia University, New York City (2004)Google Scholar
  10. 10.
    Sloman, A., Scheutz, M.: A Framework for Comparing Agent Architectures. In: Proceedings of UKCI 2002, UK Workshop on Computational Intelligence, Birmingham,UK, UK Workshop on Computational Intelligence, Birmingham (2002)Google Scholar
  11. 11.
    Brooks, R.A.: A Robust Layered Control System For A Mobile Robot. IEEE Journal Of Robotics And Automation RA-2, 14–23 (1986)Google Scholar
  12. 12.
    Roy, D., Pentland, A.: Learning words from sights and sounds: A computational model. Cognitive Science 26, 113–146 (2002)CrossRefGoogle Scholar
  13. 13.
    Gorniak, P., Roy, D.: Grounded semantic composition for visual scenes. Journal of Artificial Intelligence Research 21 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Catriona Kennedy
    • 1
  • Georgios Theodoropoulos
    • 1
  1. 1.School of Computer ScienceUniversity of BirminghamUK

Personalised recommendations