@inproceedings{135177e35d284831a0dd8f81a0502076,
title = "Strategies for Energy-Aware Multi-agent Continuous Cooperative Patrolling Problems Subject to Requirements",
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.",
keywords = "Continuous patrolling, Cooperation, Energy efficiency, Learning, Multi-agent systems",
author = "Lingying Wu and Toshiharu Sugawara",
note = "Funding Information: This work was partly supported by JSPS KAKENHI (17KT0044). Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019 ; Conference date: 28-10-2019 Through 31-10-2019",
year = "2019",
doi = "10.1007/978-3-030-33792-6_44",
language = "English",
isbn = "9783030337919",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "585--593",
editor = "Matteo Baldoni and Mehdi Dastani and Beishui Liao and Yuko Sakurai and {Zalila Wenkstern}, Rym",
booktitle = "PRIMA 2019",
}
