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To Plan or Not to Plan: Lessons Learned from Building Large Scale Social Simulations

  • Anton BogdanovychEmail author
  • Tomas Trescak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10498)

Abstract

Building large scale social simulations in virtual environments requires having a large number of virtual agents. Often we need to simulate hundreds or even thousands of individuals in order to have a realistic and believable simulation. One of the obvious desires of the developers of such simulations is to have a high degree of automation in regards to agent behaviour. The key techniques to provide this automation are: crowd simulation, planning and utility based approaches. Crowd simulation algorithms are appropriate for simulating simple pedestrian movement or for showing group activities, which do not require complex object use, but are not suitable for simulating complex everyday life, where agents need to eat, sleep, work, etc. Planning and utility based approaches remain the most suitable for this situation. In our research we are interested in developing advanced history and cultural heritage simulations and have tried to utilise planning and utility based methods (the most popular one of which is used in the game “The Sims”). Here we examine pros and cons of each of the two techniques and illustrate the key lessons that we have learned with a case study focused on developing a simulation of everyday life in ancient Mesopotamia 5000 B.C.

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References

  1. 1.
    AI Game Programmers Guild. http://gameai.com/wiki/index.php?title=The_Sims: The Sims (2011)
  2. 2.
    Champandard, A.J.: In-Depth Study of Planning in Top AAA Games. AiGameDev.com, February 28, 2014Google Scholar
  3. 3.
    Crüsemann, N., van Ess, M., Hilgert, M., Salje, B.: Uruk. 5000 Jahre Megacity (2013)Google Scholar
  4. 4.
    Esteva, M.: Electronic Institutions: From Specification to Development. Ph.D. thesis, Institut d’Investigació en Intelligència Artificial (IIIA), Spain (2003)Google Scholar
  5. 5.
    Gauder, J.: Crysis 3 cost $66 million to make, can next gen sustain such budgets? GameChup Video Games News at (2013). http://www.gamechup.com/crysis-3-cost-66-million-to-make-can-next-gen-sustain-such-budgets/
  6. 6.
    Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Morgan Kaufmann Series in Artificial Intelligence. Elsevier (2004). https://books.google.com.au/books?id=eCj3cKC_3ikCCrossRefGoogle Scholar
  7. 7.
    Hendler, J.A., Tate, A., Drummond, M.: AI planning: Systems and techniques. AI Magazine 11(2), 61 (1990)Google Scholar
  8. 8.
    Maslow, A.H.: A theory of human motivation. Psychological Review 50(4), 370–396 (1943)CrossRefGoogle Scholar
  9. 9.
    Orkin, J.: Applying goal oriented action planning in games. In: AI Game Programming Wisdom 2, pp. 217–229. Charles River Media (2002). http://web.media.mit.edu/~jorkin/GOAP_draft_AIWisdom2_2003.pdf
  10. 10.
    Trescak, T., Bogdanovych, A., Simoff, S.: Populating virtual cities with diverse physiology driven crowds of intelligent agents. In: Proceedings of the Social Simulation Conference (SSC 2014), pp. 275–286 (2014)Google Scholar
  11. 11.
    Trescak, T., Bogdanovych, A., Simoff, S., Rodriguez, I.: Generating diverse ethnic groups with genetic algorithms. In: Proceedings of the 18th ACM symposium on Virtual reality software and technology, VRST 2012, pp. 1–8. ACM, New York (2012). http://doi.acm.org/10.1145/2407336.2407338

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.MARCS Institute for Brain, Behaviour and Development, School of Computing, Engineering and MathematicsWestern Sydney UniversitySydneyAustralia

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