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2018 | OriginalPaper | Buchkapitel

A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo

verfasst von : Josue-Miguel Flores-Parra, Manuel Castañón-Puga, Carelia Gaxiola-Pacheco, Luis-Enrique Palafox-Maestre, Ricardo Rosales, Alfredo Tirado-Ramos

Erschienen in: Computer Science and Engineering—Theory and Applications

Verlag: Springer International Publishing

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Abstract

In machine learning, hybrid systems are methods that combine different computational techniques in modeling. NetLogo is a favorite tool used by scientists with limited ability as programmers who aim to leverage computer modeling via agent-oriented approaches. This paper introduces a novel modeling framework, JT2FIS NetLogo, a toolkit for integrating interval Type-2 fuzzy inference systems in agent-based models and simulations. An extension to NetLogo, it includes a set of tools oriented to data mining, configuration, and implementation of fuzzy inference systems that modeler used within an agent-based simulation. We discuss the advantages and disadvantages of integrating intelligent systems in agent-based simulations by leveraging the toolkit, and present potential areas of opportunity.

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Vorheriges Kapitel Multi-layered Network Modeled with MAS and Network Theory

Nächstes Kapitel An Approach to Fuzzy Inference System Based Fuzzy Cognitive Maps

Locations are the terminology used to describe wide geographic areas of the city that are composed of Basic Geo-Statistic Area (BGSA).

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Titel: A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo
verfasst von: Josue-Miguel Flores-Parra
Manuel Castañón-Puga
Carelia Gaxiola-Pacheco
Luis-Enrique Palafox-Maestre
Ricardo Rosales
Alfredo Tirado-Ramos
Verlag: Springer International Publishing
Buch: Computer Science and Engineering—Theory and Applications
Print ISBN: 978-3-319-74059-1

Electronic ISBN: 978-3-319-74060-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-74060-7_7

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