“Creating Life-like Autonomous Agents for Real-time Interactive Installations” by Lachenmyer and Akasha

  • ©Nathan S. Lachenmyer and Sadiya Akasha




    Creating Life-like Autonomous Agents for Real-time Interactive Installations

Program Title:

    Demo Labs



    This talk briefly describes the implementation of a complex virtual ecosystem of autonomous agents for the purpose of an art installation. The autonomous agents, called Aerobes, are inspired by the lifecycle of the Aurelia sp. jellyfish, and use artificial life techniques to simulate the behavior of two distinct types of organisms. We describe the process of using ethological research of organisms to design an artificial life system in a way that both creates a cohesive simulacrum of life-like behavior and allows for compelling interactions with audiences. We created complex behaviors for the Aerobes using low-level schemata that encapsulate individual goal-directed behaviors, and combined schemata to build behaviors that appear biomimetic. In order to give each agent the appearance of individuation, we mapped the underlying parameters of individual schemata and behaviors to personality traits to create a cohesive psychographic resource for autonomous agents that allowed for variance in decision-making and behaviors without additional computational complexity. The final artificial life system was then used to control the Aerobes in In Love With The World, a public art installation hosted at the Tate Modern’s Turbine Hall for four months.


    1. Robert Burke, Damian Isla, Marc Downie, Yuri Ivanov, and Bruce Blumberg. 2001. Creature Smarts: The Art and Architecture of a Virtual Brain. Proc. Comput. GAME Dev. Conf.(2001), 147—-166. https://doi.org/
    2. Janja Ceh, Jorge Gonzalez, Aldo S. Pacheco, and José M. Riascos. 2015. The elusive life cycle of scyphozoan jellyfish – Metagenesis revisited. Sci. Rep. 5, 1 (jul 2015), 1–13. https://doi.org/10.1038/srep12037
    3. Martin Gerlach, Beatrice Farb, William Revelle, and Luís A Nunes Amaral. 2018. A robust data-driven approach identifies four personality types across four large data sets. Nat. Hum. Behav. 2(2018), 735–742. https://doi.org/10.1038/s41562-018-0419-z
    4. Nathan S Lachenmyer and Sadiya Akasha. 2022. An Aquarium of Machines : A Physically Realized Artificial Life Simulation. Proc. ACM Comput. Graph. Interact. Tech. 5, 4 (2022). https://doi.org/10.1145/3533388
    5. Craig W. Reynolds. 1987. Flocks, herds, and schools: A distributed behavioral model. In Proc. 14th Annu. Conf. Comput. Graph. Interact. Tech. SIGGRAPH 1987. Association for Computing Machinery, Inc, 25–34. https://doi.org/10.1145/37401.37406
    6. Kingsley Stephens, Binh Pham, and Aster Wardhani. 2003. Modelling fish behaviour. In Proc. 1st Int. Conf. Comput. Graph. Interact. Tech. Australas. South East Asia, Graph. ’03. 71–78. https://doi.org/10.1145/604471.604488
    7. Xiaoyuan Tu and Demetri Terzopoulos. 1994. Artificial fishes: Physics, locomotion, perception, behavior. In Proc. 21st Annu. Conf. Comput. Graph. Interact. Tech. SIGGRAPH 1994. Association for Computing Machinery, Inc, New York, New York, USA, 43–50. https://doi.org/10.1145/192161.192170
    8. Thomas A. Widiger and Cristina Crego. 2019. The Five Factor Model of personality structure: an update. World Psychiatry 18, 3 (oct 2019), 271. https://doi.org/10.1002/WPS.20658

ACM Digital Library Publication:

Overview Page: