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Ensemble Methods for Cooperative Robotic Learning
Author(s) -
Tolmidis Avraam Th.,
Petrou Loukas
Publication year - 2017
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21858
Subject(s) - computer science , merge (version control) , robot , task (project management) , ensemble learning , artificial intelligence , machine learning , ensemble forecasting , information retrieval , engineering , systems engineering
In this paper, we examine the use of ensemble methods in a multirobot task allocation environment. The aim is to enable a robot that needs to estimate the required resources to complete a task, to utilize information coming from other robots of the same type. To our knowledge, it is the first attempt made, to use such methods, to combine data of the same type, coming from data sets of different agents, to form a prediction. Knowledge exchange is not continuous, but only ad hoc. To merge data, we use ensemble models. This keeps communication needs to a minimum, as only the models themselves—and no actual data— need to be exchanged. To further reduce communication costs, the number of robots that contribute information is being limited. Finally, we make an attempt to see how well the concept we use would perform in other domains. This is to examine whether the approach could yield the same results in other domains, or it is limited to the task allocation problem, as formulated in Tolmidis and Petrou ( Eng. Appl. Artif. Intell ., 2013;26(5–6):1458–1468) . For this, we selected two additional, different, publicly available data sets.