Abstract
This paper presents an evolutionary framework for generating diverse libraries of collective motion behaviours. It builds upon recent advancements in machine recognition of collective motion and the transformation of random motions into structured collective behaviours. The paper describes the design of the framework, including the use of a fitness function and diversity metrics specifically tailored for this purpose. The proposed framework generates diverse behaviours with distinct collective motion characteristics. Analysing the relationship between genotypic and behavioural diversity, we observed that greater diversity emerges after a moderate number of evolutionary generations. Our findings highlight the effectiveness of task non-specific fitness functions in distinguishing structured collective behaviours in an evolutionary setting.
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
Similar content being viewed by others
References
Abpeikar, S., Kasmarik, K., Garratt, M., Hunjet, R., Khan, M.M., Qiu, H.: Automatic collective motion tuning using actor-critic deep reinforcement learning. Swarm Evol. Comput. 101085 (2022)
Barlow, M., Lakshika, E.: What cost teamwork: quantifying situational awareness and computational requirements in a proto-team via multi-objective evolution. In: 2016 IEEE Congress on Evolutionary Computation (CEC) (2016)
Eshelman, L., Schaffer, J.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms, vol. 2, p. 187–202. Elsevier (1993)
Ferrante, E., Turgut, A., Stranieri, A., Pinciroli, C., Birattari, M., Dorigo, M.: A self-adaptive communication strategy for flocking in stationary and non-stationary environments. Natural Comput. 13(2), 225–245 (2014)
Gomes, J., Urbano, P., Christensen, A.: Evolution of swarm robotics systems with novelty search. Swarm Intell. 7(2–3), 115–144 (2013)
Hamann, H.: Evolution of collective behaviours by minimizing surprise. In: ALIFE2014 (2014)
Harik, G.: Finding multimodal solutions using restricted tournament selection. In: ICGA (1995)
Harvey, J., Merrick, K.E., Abbass, H.A.: Assessing human judgment of computationally generated swarming behavior. Front. Robot. AI 5, 13 (2018)
Harvey, J., Merrick, K., Abbass, H.: Quantifying swarming behaviour. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2016. LNCS, vol. 9712, pp. 119–130. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41000-5_12
Khan, M., Kasmarik, K., Barlow, M.: Autonomous detection of collective behaviours in swarms. Swarm Evol. Comput. 57, 100715 (2020)
Merrick, K., Maher, M.: Motivated Reinforcement Learning: Curious Characters for Multiuser Games. Springer, Berlin (2009). https://doi.org/10.1007/978-3-540-89187-1
Reynolds, C.: Flocks, herds and schools: a distributed behavioral model. In: Computer Graphics (SIGGRAPH 1987) Conference Proceedings, vol. 21, no. 4, pp. 25–34 (1987)
Shafi, K., Merrick, K.E., Debie, E.: Evolution of intrinsic motives in multi-agent simulations. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds.) SEAL 2012. LNCS, vol. 7673, pp. 198–207. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34859-4_20
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in matlab: the SOM toolbox. In: Proceedings of the Matlab DSP Conference (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khan, M. et al. (2024). Generating Collective Motion Behaviour Libraries Using Developmental Evolution. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14472. Springer, Singapore. https://doi.org/10.1007/978-981-99-8391-9_35
Download citation
DOI: https://doi.org/10.1007/978-981-99-8391-9_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8390-2
Online ISBN: 978-981-99-8391-9
eBook Packages: Computer ScienceComputer Science (R0)