Machine Learning in Multi-Agent Systems using Associative Arrays

Przemysław Spychalski; Ryszard Arendt

doi:10.1016/j.parco.2018.03.006

Machine Learning in Multi-Agent Systems using Associative Arrays

Abstrakt

In this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance by reducing time of processing requests, that were previously acknowledged and stored in learning module. This article contains an insight into different machine learning algorithms and includes the classification of learning techniques regarding the criteria depicted by multi-agent systems. The publication is also an attempt to provide the answer for a question posted by Shoham, Powers and Grenager: “If multi-agent learning is the answer, what is the question?”

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Autorzy (2)

Przemysław Spychalski mgr inż.
Ryszard Arendt dr hab. inż.

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Wersja publikacji: Accepted albo Published Version
Licencja: otwiera się w nowej karcie

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Informacje szczegółowe

Kategoria:: Publikacja w czasopiśmie
Typ:: artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:: PARALLEL COMPUTING nr 75, strony 88 - 99,
ISSN: 0167-8191
Język:: angielski
Rok wydania:: 2018
Opis bibliograficzny:: Spychalski P., Arendt R.: Machine Learning in Multi-Agent Systems using Associative Arrays// PARALLEL COMPUTING. -Vol. 75, (2018), s.88-99
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.parco.2018.03.006
Weryfikacja:: Politechnika Gdańska