Abstract
This paper proposes a method of autonomous strategy learning for multiple cooperative agents integrated with a series of behavioral strategies aiming at reduction of energy cost on the premise of satisfying quality requirements in continuous patrolling problems. We improved our algorithm of requirement estimation to avoid concentration of agents since they are given the knowledge of the work environment in advance. The experimental results show that our proposal enables the agents to learn to select appropriate behavioral planning strategies according to performance efficiency and energy cost, and to individually estimate whether the given requirement is reached and modify their action plans to save energy. Furthermore, agents with the new requirement estimation method could achieve fair patrolling by introducing local observations.
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References
Almeida, A., et al.: Recent advances on multi-agent patrolling. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 474–483. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28645-5_48
Iocchi, L., Marchetti, L., Nardi, D.: Multi-robot patrolling with coordinated behaviours in realistic environments. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2796–2801, September 2011
Milech Cabreira, T., Stift Kappel, K., Ferreira, P.R., Brisolara de Brisolara, L.: An energy-aware real-time search approach for cooperative patrolling missions with multi-UAVs. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE). pp. 254–259, November 2018
Sugiyama, A., Sea, V., Sugawara, T.: Effective task allocation by enhancing divisional cooperation in multi-agent continuous patrolling tasks. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 33–40, November 2016
Sugiyama, A., Sugawara, T.: Meta-strategy for cooperative tasks with learning of environments in multi-agent continuous tasks. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC 2015, pp. 494–500. ACM, New York (2015)
Sugiyama., A., Wu., L., Sugawara., T.: Learning of activity cycle length based on battery limitation in multi-agent continuous cooperative patrol problems (2019)
Wu, L., Sugiyama, A., Sugawara, T.: Energy-efficient strategies for multi-agent continuous cooperative patrolling problems. In: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference, KES-2019. Budapest, Hungary, September 2019
Yoneda, K., Sugiyama, A., Kato, C., Sugawara, T.: Learning and relearning of target decision strategies in continuous coordinated cleaning tasks with shallow coordination. Web Intell. 13(4), 279–294 (2015)
Mei, Y., Lu, Y.-H., Lee, C.S.G., Hu, Y.C.: Energy-efficient mobile robot exploration. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA 2006), pp. 505–511, May 2006
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This work was partly supported by JSPS KAKENHI (17KT0044).
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Wu, L., Sugawara, T. (2019). Strategies for Energy-Aware Multi-agent Continuous Cooperative Patrolling Problems Subject to Requirements. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_44
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DOI: https://doi.org/10.1007/978-3-030-33792-6_44
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