Understanding of Collective Decision-Making in Natural Swarms System, Applications and Challenges

Muhammad Abu Bakar


Swarm robotics is an emergent field that is inspired from biological system. Decentralized control for the achievement of global task. The concept of collective system is used to develop artificial systems that can perform the tasks in a collective manner, with minimum resources. In this paper we have presented group level intelligence to achieve global goals. Optimization strategies for swarm robotics, self-learning of agents by using trial and error, a well know technique of reinforcement learning and how system is designed to minimize the task allocation. We discuss some of the enabling factors at micro and macro-level and how these factors affect the modelling and intelligence of artificial system. Using the modern artificial intelligence, designing new systems to solve the complex research problems. And some of the key challenges in the field of swarm intelligence.


Applications; Challenges; Natural Swarms System

Full Text:



Bernstein, D. S., Givan, R., Immerman, N., and Zilberstein, S. (2002). The complexity of decentralized control of Markov decision processes. Mathematics of operations research, 27(4), 819-840.

Cortes, J., Martinez, S., Karatas, T., and Bullo, F. (2004). Coverage control for mobile sensing networks. IEEE Transactions on robotics and Automation, 20(2), 243-255.

Dorigo, M., Birattari, M., and Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.

Dudek, G., Jenkin, M. R., Milios, E., and Wilkes, D. (1996). A taxonomy for multi-agent robotics. Autonomous Robots, 3(4), 375-397.

Gomes, J., Urbano, P., and Christensen, A. L. (2013). Evolution of swarm robotics systems with novelty search. Swarm Intelligence, 7(2), 115-144.

Krause, J., Ruxton, G. D., and Krause, S. (2010). Swarm intelligence in animals and humans. Trends in ecology & evolution, 25(1), 28-34.

Krause, S., James, R., Faria, J. J., Ruxton, G. D., and Krause, J. (2011). Swarm intelligence in humans: Diversity can trump ability. Animal Behaviour, 81(5), 941-948.

Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011). How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences, 108(22), 9020-9025.

Martinoli, A., Easton, K., and Agassounon, W. (2004). Modeling swarm robotic systems: A case study in collaborative distributed manipulation. The International Journal of Robotics Research, 23(4-5), 415-436.

O’Bryan, L., Beier, M., and Salas, E. (2020). How approaches to animal swarm intelligence can improve the study of collective intelligence in human teams. Journal of Intelligence, 8(1), 9.

Prasetyo, J., De Masi, G., and Ferrante, E. (2019). Collective decision making in dynamic environments. Swarm Intelligence, 13(3), 217-243.

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., and Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686-688.

Yang, S., Li, M., and Wu, J. (2007). Scan-based movement-assisted sensor deployment methods in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 18(8), 1108-1121.

DOI: https://doi.org/10.17509/ajse.v1i3.39637


  • There are currently no refbacks.

Copyright (c) 1970 Universitas Pendidikan Indonesia

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

ASEAN Journal of Science and Engineering (AJSE) is published by UPI 

View My Stats