Abstract
This paper presents a framework, called the knowledge co-creation framework (KCF), for the heterogeneous multi-robot transfer learning method with utilization of cloud-computing resources. A multi-agent robot system (MARS) that utilizes reinforcement learning and transfer learning methods has recently been deployed in real-world situations. In MARS, autonomous agents obtain behavior autonomously through multi-agent reinforcement learning and the transfer learning method enables the reuse of the knowledge of other robots’ behavior, such as for cooperative behavior. These methods, however, have not been fully and systematically discussed. To address this, KCF leverages the transfer learning method and cloud-computing resources. In prior research, we developed a hierarchical transfer learning (HTL) method as the core technology of knowledge co-creation and investigated its effectiveness in a dynamic multi-agent environment. The HTL method hierarchically abstracts obtained knowledge by ontological methods. Here, we evaluate the effectiveness of HTL with two types of ontology: action and state.
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
Sugiyama, H., Tsujioka, T., Murata, M.: Coordination of rescue robots for real-time exploration over disaster areas. In: The 11th IEEE International Symposium on Object Oriented Real-Time Distributed Computing, Orlando, FL, pp. 170–177 (2008)
Marino, A., Parker, L.E., Antonelli, G., Caccavale, F.: A decentralized architecture for multi-robot systems based on the null-space-behavioral control with application to multi-robot border patrolling. J. Intell. Robot. Syst. (2012). doi:10.1007/s10846-012-9783-5
d’Andrea, R.: Guest editorial: a revolution in the warehouse: a retrospective on Kiva systems and the grand challenges ahead. IEEE Trans. Autom. Sci. Eng. 9(4), 638–639 (2012)
Tan, M.: Multi-agent reinforcement learning: independent vs. cooperative agents. In: 10th International Conference on Machine learning, pp. 330–337 (1993)
Arai, S., Sycara, K., Payne, T.R.: Experience-based reinforcement learning to acquire effective behavior in a multi-agent domain. In: PRICAI 2000 Topics in Artificial Intelligence, pp. 125–135 (2000)
Yang, E., Gu, D.: A survey on multiagent reinforcement learning towards multi-robot systems. In: IEEE Symposium on Computational Intelligence and Games, ID2012, Essex, UK (2005)
Matarić, M.: Reinforcement learning in the multi-robot domain. Auton. Robots 4(1), 73–83 (1997)
Waibel, M., Beetz, M., Civera, J., D’Andrea, R., Elfring, J., Galván-López, D., Haussermann, K., Janssen, R., Montiel, J.M., Perzylo, A., Schießle, B., Tenorth, M., Zweigle, O., van de Molengraft, R.: A world wide web for robots roboearth. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011)
Hu, G., Tay, W.P., Wen, Y.: Cloud robotics: architecture, challenges and applications. IEEE Netw. 26(3), 21–28 (2012)
Kono, H., Sawai, K., Suzuki, T.: Convergence estimation utilizing fractal dimensional analysis for reinforcement learning. In: SICE Annual Conference 2013, pp. 2752–2757 (2013)
Kono, H., Kamimura, A., Tomita, K., Murata, Y., Suzuki, T.: Transfer learning method utilizing ontology for heterogeneous multi-agent reinforcement learning. Int. J. Adv. Comput. Sci. Appl. 5(10), 156–164 (2014)
Taylor, M.E.: Transfer in Reinforcement Learning Domains. Studies in Computational Intelligence, vol. 216. Springer, New York (2009)
Boutsioukis, G., Partalas, I., Vlahavas, I.: Transfer Learning in Multi-agent Reinforcement Learning Domains. Recent Advances in Reinforcement, Learning, pp. 249–260 (2012)
Taylor, A., Dusparic, I., Galvn-Lpez, E., Clarke, S., Cahill, V.: Transfer learning in multi-agent systems through parallel transfer. In: 30th International Conference on Machine Learning (2013)
Wache, H., Voegele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Hbner, S.: Ontology-based integration of information—a survey of existing approaches. In: IJCAI-01 Workshop: Ontologies and Information Sharing, pp. 108-117 (2001)
Acknowledgments
This work was partially supported by Research Institute for Science and Technology of Tokyo Denki University Grant Number Q14J-01 Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Japan
About this paper
Cite this paper
Kono, H., Murata, Y., Kamimura, A., Tomita, K., Suzuki, T. (2016). Knowledge Co-creation Framework: Novel Transfer Learning Method in Heterogeneous Multi-agent Systems. In: Chong, NY., Cho, YJ. (eds) Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 112 . Springer, Tokyo. https://doi.org/10.1007/978-4-431-55879-8_27
Download citation
DOI: https://doi.org/10.1007/978-4-431-55879-8_27
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-55877-4
Online ISBN: 978-4-431-55879-8
eBook Packages: EngineeringEngineering (R0)