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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barve, S.S.: Dynamic decision model using partially observable Markov decision process. Int. J. Emerg. Trend Eng. Basic Sci. 2(1), 785–788 (2015)
Browne, C.B., et al.: A survey of monte carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4(1), 1–43 (2012)
Epstein, J.M.: Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press, Princeton (2006)
Head, B., Hjorth, A., Brady, C., Wilensky, U.: Evolving agent cognition with Netlogo LevelSpace. In: Proceedings of the Winter Simulation Conference (2015)
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
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)
Lovejoy, W.S.: A survey of algorithmic methods for partially observed Markov decision processes. Ann. Oper. Res. 28(1), 47–65 (1991)
Macal, C.M., North, M.J.: Agent-based modeling and simulation: ABMS examples. In: Winter Simulation Conference, WSC 2008, pp. 101–112. IEEE (2008)
Morvan, G.: Multi-level agent-based modeling: A Literature Survey. CoRR abs/1205.0 (2013)
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
Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Silver, D., et al.: Mastering the game of Go without human knowledge. Nature 550(7676), 354–359 (2017)
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
Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999). http://ccl.northwestern.edu/netlogo/
Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press, Cambridge (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Head, B., Wilensky, U. (2018). Agent Cognition Through Micro-simulations: Adaptive and Tunable Intelligence with NetLogo LevelSpace. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-96661-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96660-1
Online ISBN: 978-3-319-96661-8
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)