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Authors: Ayumi Sugiyama ; Lingying Wu and Toshiharu Sugawara

Affiliation: Department Computer Science and Communications Engineering, Waseda University, Tokyo 1698555 and Japan

ISBN: 978-989-758-350-6

Keyword(s): Continuous Cooperative Patrol Problem, Cycle Learning, Multi-agent, Division of Labor, Battery Limitation.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Autonomous Systems ; Cooperation and Coordination ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Software Engineering ; Symbolic Systems ; Task Planning and Execution

Abstract: We propose a learning method that decides the appropriate activity cycle length (ACL) according to environmental characteristics and other agents’ behavior in the (multi-agent) continuous cooperative patrol problem. With recent advances in computer and sensor technologies, agents, which are intelligent control programs running on computers and robots, obtain high autonomy so that they can operate in various fields without pre-defined knowledge. However, cooperation/coordination between agents is sophisticated and complicated to implement. We focus on the ACL which is time length from starting patrol to returning to charging base for cooperative patrol when agents like robots have batteries with limited capacity. Long ACL enable agent to visit distant location, but it requires long rest. The basic idea of our method is that if agents have long-life batteries, they can appropriately shorten the ACL by frequently recharging. Appropriate ACL depends on many elements such as environmental size, the number of agents, and workload in an environment. Therefore, we propose a method in which agents autonomously learn the appropriate ACL on the basis of the number of events detected per cycle. We experimentally indicate that our agents are able to learn appropriate ACL depending on established spatial divisional cooperation. (More)

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Paper citation in several formats:
Sugiyama, A.; Wu, L. and Sugawara, T. (2019). Learning of Activity Cycle Length based on Battery Limitation in Multi-agent Continuous Cooperative Patrol Problems.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-350-6, pages 62-71. DOI: 10.5220/0007567400620071

@conference{icaart19,
author={Ayumi Sugiyama. and Lingying Wu. and Toshiharu Sugawara.},
title={Learning of Activity Cycle Length based on Battery Limitation in Multi-agent Continuous Cooperative Patrol Problems},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2019},
pages={62-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007567400620071},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Learning of Activity Cycle Length based on Battery Limitation in Multi-agent Continuous Cooperative Patrol Problems
SN - 978-989-758-350-6
AU - Sugiyama, A.
AU - Wu, L.
AU - Sugawara, T.
PY - 2019
SP - 62
EP - 71
DO - 10.5220/0007567400620071

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