Advertisement

Enhancing Multi-Agent Based Simulation with Human-Like Decision Making Strategies

  • Emma Norling
  • Liz Sonenberg
  • Ralph Rönnquist
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1979)

Abstract

We are exploring the enhancement of models of agent behaviour with more “human-like” decision making strategies than are presently available. Our motivation is to build multi-agent based simulations of human societies that would exhibit more realistic simulation behaviours. This in turn will allow researchers to study more complex issues regarding individual and organisational performance than is possible with present systems. Drawing on studies into naturalistic decision making, which looks at people’s decision making in their natural environments, we propose ways of integrating a recognition-primed decision making approach with the popular BDI agent architecture, and report on preliminary empirical studies.

Keywords

Naturalistic Decision Plan Library Agent Orient Software Morning Rush Ablex Publishing Corporation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agent Oriented Software Pty. Ltd. JACK Intelligent Agents. URL: http://www.agent-software.com.au/jack.html. 220
  2. 2.
    John R. Anderson and Christian Lebiere. The Atomic Components of Thought. Lawrence Eribaum Associates, 1998. 215Google Scholar
  3. 3.
    Michael E. Bratman. Intention, Plans and Practical Reason. Harvard University Press, Cambridge, Massachusets, 1987. 219Google Scholar
  4. 4.
    Michael E. Bratman, David J. Israel, and Martha E. Pollack. Plans and resourcebounded practical reasoning. Computational Intelligence, 4(4):349–355, 1988. 219CrossRefGoogle Scholar
  5. 5.
    CHI Systems Inc. COGNET-COGnition as a NEtwork of Tasks. URL: http://www.chiinc.com/cognethome.shtml. 215
  6. 6.
    M. d’lnverno, D. Kinny, M. Luck, and M. Wooldridge. A formal specification of dMARS. In M. P. Singh, A. Rao, and M. J. Wooldridge, editors, Intelligent Agents IV (LNAI Volume 1365), volume 1365 of Springer Lecture Notes in Artificial Intelligence, pages 155–176. Springer, 1997. 215, 220Google Scholar
  7. 7.
    Hubert L. Dreyfus. Intuitive, deliberative and calculative models of expert performance. In Caroline E. Zsambok and Gary Klein, editors, Naturalistic Decision Making, pages 17–28. Lawrence Eribaum Associates, 1997. 217Google Scholar
  8. 8.
    Bruce Edmonds. The pragmatic roots of context. In Modeling and Using Context: the Proceedings of CONTEXT’99, volume 1688of Springer Lecture Notes in Artificial Intelligence, pages 119–132, Trento, Italy, 1999. Springer-Verlag. 223Google Scholar
  9. 9.
    Rhona Flin, Keith Stewart, and Georgina Slaven. Emergency decision making in the offshore oil and gas industry. Human Factors, 38(2):262–277,1996221CrossRefGoogle Scholar
  10. 10.
    Glint Heinze, Bradley Smith, and Martin Cross. Thinking quickly: Agents for modeling air warfare. In Proceedings of the Eighth Australian Joint Conference on Artificical Intelligence, Brisbane, Australia, 1998. 216Google Scholar
  11. 11.
    Randolph M. Jones, John E. Laird, Paul E. Nielsen, Karen J. Coulter, Patrick Kenny, and Frank V. Koss. Automated intelligent pilots for combat flight simulation. AI Magazine, Spring:27–41, 1999. 214Google Scholar
  12. 12.
    Gary Klein. Sources of Power. The MIT Press, 1998. 217, 218Google Scholar
  13. 13.
    Janet Kolodner. Case-Based Reasoning. Morgan Kaufmann Publishers, 1993. 221Google Scholar
  14. 14.
    John E. Laird. It knows what you’re going to do: Adding anticipation to a quakebot. Technical Report SS-00–02, AAAI, March 2000. 216Google Scholar
  15. 15.
    John E. Laird, Alien Newell, and Paul S. Rosenbloom. Soar: An architecture for general intelligence. Artificial Intelligence, 33(1): 1–64, 1987. 215CrossRefMathSciNetGoogle Scholar
  16. 16.
    Raanan Lipshitz. Converging themes in the study of decision making in realistic settings. In Gary A. Klein, Judith Orasanu, Roberta Calderwood, and Caroline E. Zsambok, editors, Decision Making in Action: Models and Methods, pages 103–137. Ablex Publishing Corporation, 1993. 217Google Scholar
  17. 17.
    B. A. Mellers, A. Schwartz, and A. D. J. Cooke. Judgement and decision making. Annual Review of Psychology, 49:447–478, 1998. 214CrossRefGoogle Scholar
  18. 18.
    Judith Orasanu and Terry Connolly. The reinvention of decision making. In Gary A. Klein, Judith Orasanu, Roberta Calderwood, and Caroline E. Zsambok, editors, Decision Making in Action: Models and Methods, pages 3–20. Ablex Publishing Corporation, 1993. 217Google Scholar
  19. 19.
    Anand S. Rao and Michael P. George.. Modelling rational agents within a BDI architecture. In J. Alien, R. Fikes, and E. Sandewall, editors, Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KRR-91). Kaufmann, 1991. 215Google Scholar
  20. 20.
    Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998. 222Google Scholar
  21. 21.
    Gil Tidhar, Clinton Heinze, and Mario Selvestrel. Flying together: Modelling air mission teams. Applied Intelligence, 8(3): 195–218,1998214CrossRefGoogle Scholar
  22. 22.
    Caroline E. Zsambok. Naturalistic decision making:Where are we now? In Caroline E. Zsambok and Gary Klein, editors, Naturalistic Decision Making, pages 3–16. Lawrence Eribaum Associates, 1997. 216Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Emma Norling
    • 1
  • Liz Sonenberg
    • 2
  • Ralph Rönnquist
    • 3
  1. 1.Computer Science and Software EngineeringThe University of MelbourneUSA>
  2. 2.Department of Information SystemsThe University of MelbourneUSA>
  3. 3.Agent Oriented Software Pty LtdUSA>

Personalised recommendations