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Agent Cognition Through Micro-simulations: Adaptive and Tunable Intelligence with NetLogo LevelSpace

  • Bryan Head
  • Uri Wilensky
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

We present a method of endowing agents in an agent-based model (ABM) with sophisticated cognitive capabilities and a naturally tunable level of intelligence. Often, ABMs use random behavior or greedy algorithms for maximizing objectives (such as a predator always chasing after the closest prey). However, random behavior is too simplistic in many circumstances and greedy algorithms, as well as classic AI planning techniques, can be brittle in the context of the unpredictable and emergent situations in which agents may find themselves. Our method, called agent-centric Monte Carlo cognition (ACMCC), centers around using a separate agent-based model to represent the agents’ cognition. This model is then used by the agents in the primary model to predict the outcomes of their actions, and thus guide their behavior. To that end, we have implemented our method in the NetLogo agent-based modeling platform, using the recently released LevelSpace extension, which we developed to allow NetLogo models to interact with other NetLogo models. As an illustrative example, we extend the Wolf Sheep Predation model (included with NetLogo) by using ACMCC to guide animal behavior, and analyze the impact on agent performance and model dynamics. We find that ACMCC provides a reliable and understandable method of controlling agent intelligence, and has a large impact on agent performance and model dynamics even at low settings.

Keywords

Agent-based modeling Artificial intelligence Agent cognition Multi-level agent-based modeling NetLogo 

References

  1. 1.
    Barve, S.S.: Dynamic decision model using partially observable Markov decision process. Int. J. Emerg. Trend Eng. Basic Sci. 2(1), 785–788 (2015)Google Scholar
  2. 2.
    Browne, C.B., et al.: A survey of monte carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4(1), 1–43 (2012)CrossRefGoogle Scholar
  3. 3.
    Epstein, J.M.: Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press, Princeton (2006)MATHGoogle Scholar
  4. 4.
    Head, B., Hjorth, A., Brady, C., Wilensky, U.: Evolving agent cognition with Netlogo LevelSpace. In: Proceedings of the Winter Simulation Conference (2015)Google Scholar
  5. 5.
    Hjorth, A., Head, B., Wilensky, U.: LevelSpace NetLogo extension. Center for Connected Learning and Computer Based Modeling, Northwestern University, Evanston, IL (2015). http://ccl.northwestern.edu/levelspace/index.html
  6. 6.
    Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Cohen, W.W., Hirsh, H. (eds.): Machine Learning Proceedings 1994, pp. 157–163. Morgan Kaufmann, San Francisco (CA) (1994)Google Scholar
  7. 7.
    Lovejoy, W.S.: A survey of algorithmic methods for partially observed Markov decision processes. Ann. Oper. Res. 28(1), 47–65 (1991)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Macal, C.M., North, M.J.: Agent-based modeling and simulation: ABMS examples. In: Winter Simulation Conference, WSC 2008, pp. 101–112. IEEE (2008)Google Scholar
  9. 9.
    Morvan, G.: Multi-level agent-based modeling: A Literature Survey. CoRR abs/1205.0 (2013)Google Scholar
  10. 10.
    Rabinowitz, N.C., Perbet, F., Song, H.F., Zhang, C., Eslami, S.M.A., Botvinick, M.: Machine Theory of Mind. arXiv:1802.07740 [cs], February 2018
  11. 11.
    Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)ADSCrossRefGoogle Scholar
  12. 12.
    Silver, D., et al.: Mastering the game of Go without human knowledge. Nature 550(7676), 354–359 (2017)ADSCrossRefGoogle Scholar
  13. 13.
    Wilensky, U.: NetLogo Wolf Sheep Predation Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1997). http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation
  14. 14.
    Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999). http://ccl.northwestern.edu/netlogo/
  15. 15.
    Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press, Cambridge (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Center for Connected Learning and Computer-Based Modeling, Northwestern Institute of Complex Systems, Department of EECSNorthwestern UniversityEvanstonUSA
  2. 2.Department of Learning SciencesNorthwestern UniversityEvanstonUSA

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