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Machine Learning in Multi-Agent Systems using Associative Arrays

Abstract

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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Category:
Articles
Type:
artykuł w czasopiśmie wyróżnionym w JCR
Published in:
PARALLEL COMPUTING no. 75, pages 88 - 99,
ISSN: 0167-8191
Language:
English
Publication year:
2018
Bibliographic description:
Spychalski P., Arendt R.: Machine Learning in Multi-Agent Systems using Associative Arrays// PARALLEL COMPUTING. -Vol. 75, (2018), s.88-99
DOI:
Digital Object Identifier (open in new tab) 10.1016/j.parco.2018.03.006
Bibliography: test
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