Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems

Villaverde San José, Mónica and Pérez Daza, David and Moreno González, Félix Antonio (2014). Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems. In: "IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society", 30 oct - 01 nov 2014, Dallas (USA). ISBN 978-1-4799-4032-5. pp. 2742-2730.

Description

Title: Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems
Author/s:
  • Villaverde San José, Mónica
  • Pérez Daza, David
  • Moreno González, Félix Antonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society
Event Dates: 30 oct - 01 nov 2014
Event Location: Dallas (USA)
Title of Book: Proceedings IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society
Date: 2014
ISBN: 978-1-4799-4032-5
Subjects:
Freetext Keywords: embedded artificial intelligence; learning systems; cooperative systems; intelligent agents; adaptive systems; weighting procedures; decision making
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Cooperative systems are suitable for many types of applications and nowadays these system are vastly used to improve a previously defined system or to coordinate multiple devices working together. This paper provides an alternative to improve the reliability of a previous intelligent identification system. The proposed approach implements a cooperative model based on multi-agent architecture. This new system is composed of several radar-based systems which identify a detected object and transmit its own partial result by implementing several agents and by using a wireless network to transfer data. The proposed topology is a centralized architecture where the coordinator device is in charge of providing the final identification result depending on the group behavior. In order to find the final outcome, three different mechanisms are introduced. The simplest one is based on majority voting whereas the others use two different weighting voting procedures, both providing the system with learning capabilities. Using an appropriate network configuration, the success rate can be improved from the initial 80% up to more than 90%.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainITP-2011- 1977-920000INNPACTO-2011UnspecifiedUnspecified

More information

Item ID: 37018
DC Identifier: http://oa.upm.es/37018/
OAI Identifier: oai:oa.upm.es:37018
Official URL: http://ieeexplore.ieee.org/document/7048892/
Deposited by: Memoria Investigacion
Deposited on: 10 Mar 2016 17:20
Last Modified: 23 Feb 2017 17:34
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