Modeling emergence by integrating DEVS and machine learning

Analyzing complex adaptive systems is always a challenging task. Nature and its underlying governing rules do not lways show clear patterns. The hypothesis of mergent properties in such systems is hard to formulate and difficult to infer. In his context, a great effort is being done by the Modeling...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Foguelman, D., Castro, R., Limere V., Claeys D.
Formato: CONF
Materias:
Boids
Complex adaptive systems
DEVS
Emergence
Machine learning.
Modeling
Simulation
Adaptive systems
Artificial intelligence
Behavioral research
Learning systems
Modal analysis
Models
Specifications
Discrete event simulation
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_97894928_v_n_p44_Foguelman
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_97894928_v_n_p44_Foguelman
record_format dspace
spelling todo:paper_97894928_v_n_p44_Foguelman2023-10-03T16:45:24Z Modeling emergence by integrating DEVS and machine learning Foguelman, D. Castro, R. Limere V. Claeys D. Boids Complex adaptive systems DEVS Emergence Machine learning. Modeling Simulation Adaptive systems Artificial intelligence Behavioral research Learning systems Modal analysis Models Specifications Boids Complex adaptive systems DEVS Emergence Simulation Discrete event simulation Analyzing complex adaptive systems is always a challenging task. Nature and its underlying governing rules do not lways show clear patterns. The hypothesis of mergent properties in such systems is hard to formulate and difficult to infer. In his context, a great effort is being done by the Modeling and Simulation (M&S)community towards modeling and handling mergent behavior. Our research proposes minimal modifications to the Discrete Event System Specification (DEVS) M&S framework that brings the detection of emergent behavior nto the loop of a DEVS simulation. New knowledge about behavior icro levels is learned dynamically and encoded into the DEVS layered structure at macro levels. The approach bridges the gap etween micro and macro representations of a given system. A proof of concept was implemented for the canonical Boids odel showing promising results. © 2018 PDF-CONFERENCE. All rights reserved. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97894928_v_n_p44_Foguelman
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Boids
Complex adaptive systems
DEVS
Emergence
Machine learning.
Modeling
Simulation
Adaptive systems
Artificial intelligence
Behavioral research
Learning systems
Modal analysis
Models
Specifications
Boids
Complex adaptive systems
DEVS
Emergence
Simulation
Discrete event simulation
spellingShingle Boids
Complex adaptive systems
DEVS
Emergence
Machine learning.
Modeling
Simulation
Adaptive systems
Artificial intelligence
Behavioral research
Learning systems
Modal analysis
Models
Specifications
Boids
Complex adaptive systems
DEVS
Emergence
Simulation
Discrete event simulation
Foguelman, D.
Castro, R.
Limere V.
Claeys D.
Modeling emergence by integrating DEVS and machine learning
topic_facet Boids
Complex adaptive systems
DEVS
Emergence
Machine learning.
Modeling
Simulation
Adaptive systems
Artificial intelligence
Behavioral research
Learning systems
Modal analysis
Models
Specifications
Boids
Complex adaptive systems
DEVS
Emergence
Simulation
Discrete event simulation
description Analyzing complex adaptive systems is always a challenging task. Nature and its underlying governing rules do not lways show clear patterns. The hypothesis of mergent properties in such systems is hard to formulate and difficult to infer. In his context, a great effort is being done by the Modeling and Simulation (M&S)community towards modeling and handling mergent behavior. Our research proposes minimal modifications to the Discrete Event System Specification (DEVS) M&S framework that brings the detection of emergent behavior nto the loop of a DEVS simulation. New knowledge about behavior icro levels is learned dynamically and encoded into the DEVS layered structure at macro levels. The approach bridges the gap etween micro and macro representations of a given system. A proof of concept was implemented for the canonical Boids odel showing promising results. © 2018 PDF-CONFERENCE. All rights reserved.
format CONF
author Foguelman, D.
Castro, R.
Limere V.
Claeys D.
author_facet Foguelman, D.
Castro, R.
Limere V.
Claeys D.
author_sort Foguelman, D.
title Modeling emergence by integrating DEVS and machine learning
title_short Modeling emergence by integrating DEVS and machine learning
title_full Modeling emergence by integrating DEVS and machine learning
title_fullStr Modeling emergence by integrating DEVS and machine learning
title_full_unstemmed Modeling emergence by integrating DEVS and machine learning
title_sort modeling emergence by integrating devs and machine learning
url http://hdl.handle.net/20.500.12110/paper_97894928_v_n_p44_Foguelman
work_keys_str_mv AT foguelmand modelingemergencebyintegratingdevsandmachinelearning
AT castror modelingemergencebyintegratingdevsandmachinelearning
AT limerev modelingemergencebyintegratingdevsandmachinelearning
AT claeysd modelingemergencebyintegratingdevsandmachinelearning
_version_ 1782024184545673216

Ejemplares similares