TY - GEN
T1 - Balanced team formation for tasks with deadlines
AU - Kawaguchi, Ryutaro
AU - Hayano, Masashi
AU - Sugawara, Toshiharu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/2
Y1 - 2016/2/2
N2 - A balanced team formation method is described for tasks with deadlines in multi-agent systems. With the advances that have been made in computer and network technologies, tasks that are achieved by multiple software/hardware entities are often required in many real-world applications. In addition, these tasks are usually required to be done by specified deadlines to avoid a failure of services or to provide quality services in a timely manner. We designed a method for effective team formation for cooperative work of different entities, called agents, to execute tasks having deadlines. The feature of our method is that rational agents autonomously learn which team they should join and which agents they should work with in order to improve the received rewards. Agents using the method also tried to select teams consisting of agents comparable with themselves; this can help them avoid binding to their teams unnecessarily. Another feature is that they estimate the duration of task execution to avoid a failure of tasks due to a violation of time requirements. We experimentally show that these three functions mutually affect each other positively and can achieve quite good performance in real-time environments.
AB - A balanced team formation method is described for tasks with deadlines in multi-agent systems. With the advances that have been made in computer and network technologies, tasks that are achieved by multiple software/hardware entities are often required in many real-world applications. In addition, these tasks are usually required to be done by specified deadlines to avoid a failure of services or to provide quality services in a timely manner. We designed a method for effective team formation for cooperative work of different entities, called agents, to execute tasks having deadlines. The feature of our method is that rational agents autonomously learn which team they should join and which agents they should work with in order to improve the received rewards. Agents using the method also tried to select teams consisting of agents comparable with themselves; this can help them avoid binding to their teams unnecessarily. Another feature is that they estimate the duration of task execution to avoid a failure of tasks due to a violation of time requirements. We experimentally show that these three functions mutually affect each other positively and can achieve quite good performance in real-time environments.
UR - http://www.scopus.com/inward/record.url?scp=85028327121&partnerID=8YFLogxK
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U2 - 10.1109/WI-IAT.2015.57
DO - 10.1109/WI-IAT.2015.57
M3 - Conference contribution
AN - SCOPUS:85028327121
T3 - Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
SP - 234
EP - 241
BT - Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015
Y2 - 6 December 2015 through 9 December 2015
ER -