Abstract
Groups of interacting agents are able to solve complex tasks in a dynamic environment. Robots in a group can have a simpler device than single stand-alone robots. Each agent in the group has the ability to accumulate interaction experience with the environment and share it with other members of the group. In many cases, the group behavior is not deduced from any properties of its parts. The paper proposes an approach to modeling the mobile agent group behavior that is busy with a common goal. The main purpose of the agents is to study the greatest territory at minimal time. The agents interact with the environment. A control of each agent is carried out by a modified neural network with restrictions imposed on it. Weights of the neural network are chosen by a genetic evolution method. The agents compete among themselves for obtaining the greatest reward from the environment. An efficiency of the proposed model is confirmed by some convergence speed tests by computer simulation. The proposed model can be applied to a group of robots that perform search tasks in a real physical space.
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
Maza, I., Caballero, F., Capitan, J., Martinez-de-Dios, J.M., Ollero, A.: A distributed architecture for a robotic platform with aerial sensor transportation and self-deployment capabilities. J. Field Robot. 28(3), 303–328 (2011)
De Greeff, J., Hindriks, K., Neerincx, M.A., Kruijff-Korbayova, I.: Human-robot teamwork in USAR environments: the TRADR project. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, pp. 151–152 (2015)
Nelson, E., Micah, C., Nathan, M.: Environment model adaptation for mobile robot exploration. Auton. Robots 42(2), 257–272 (2018)
Couceiro, M.S., Rocha, R.P., Ferreira, N.M.F.: Fault-tolerance assessment of a darwinian swarm exploration algorithm under communication constraints. In: 2013 IEEE International Conference on Robotics and Automation, pp. 2008–2013 (2013)
Couceiro, M.S., Figueiredo, C.M., Rocha, R.P., Ferreira, N.M.: Darwinian swarm exploration under communication constraints: initial deployment and fault-tolerance assessment. Robot. Auton. Syst. 62(4), 528–544 (2014)
Pshikhopov, V.: Path Planning for Vehicles Operating in Uncertain 2D Environments. Butterworth-Heinemann, Oxford (2017)
Izquierdo, E.J., Beer, R.D.: The whole worm: brain–body–environment models of C. elegans. Curr. Opin. Neurobiol. 40, 23–30 (2016)
Krishnanand, K.N., Amruth, P., Guruprasad, M.H., Bidargaddi, S.V., Ghose, D.: Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 958–963 (2006)
Pshikhopov, V., Medvedev, M., Gaiduk, A., Neydorf, R., Belyaev, V., Fedorenko, R., Krukhmalev, V.: Mathematical model of robot on base of airship. In: 52nd IEEE Conference on Decision and Control, pp. 959–964 (2013)
Finn, C., Tan, X.Y., Duan, Y., Darrell, T., Levine, S., Abbeel, P.: Learning visual feature spaces for robotic manipulation with deep spatial autoencoders. arXiv preprint. arXiv:1509.06113 (2015)
Petrushin, A., Ferrara, L., Blau, A.: The Si elegans project at the interface of experimental and computational Caenorhabditis elegans neurobiology and behavior. J. Neural Eng. 13(6), 065001 (2016)
Sarma, G.P., Lee, C.W., Portegys, T., Ghayoomie, V., Jacobs, T., Alicea, B., Cantarelli, M., Currie, M., Gerkin, R.C., Gingell, S., Gleeson, P.: OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans. Philos. Trans. Royal Soc. B 373(1758), 20170382 (2018)
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., Zhang, X.: End to end learning for self-driving cars. arXiv preprint. arXiv:1604.07316 (2016)
Akzhalova, A., Inoue, A., Mukharsky, D.: Intelligent mobile agents for disaster response: survivor search and simple communication support. In: AROB 2014 International Symposium on Artificial Life and Robotics, pp. 254–259 (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Akzhalova, A., Inoue, A., Mukharsky, D. (2020). Evolutionary Strategies of Intelligent Agent Training. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-30604-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-30604-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30603-8
Online ISBN: 978-3-030-30604-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)