Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents
In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Machine Learning System (MLS) based on bio-inspired techniques such as Extended Classifier Sys...
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ir-p17054coll23-4882020-09-29 Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents Romero López, Oscar Javier In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Machine Learning System (MLS) based on bio-inspired techniques such as Extended Classifier Systems (XCS), Artificial Immune Systems (AIS), Neuro Connectionist Q-Learning (NQL) and Learning Classifier Systems (LCS) among others. In this research an evolutionary mechanism based on Gene Expression Programming (GEP) to self-configure the behaviour arbitration between layers is suggested. In addition, a co-evolutionary mechanism to evolve behaviours in an independent and parallel fashion is used. The proposed approach was tested in an animat environment using a multi-agent platform and it exhibited several learning capabilities and emergent properties for self-configuring internal agent’s architecture. Bio-inspired machine learning; Gene expression programming; Hybrid behaviour; Co-evolution; Subsumption architecture Tecnología; Tecnología / Ingeniería y operaciones afines 2007 PDF Artículo ENG - Inglés Colfuturo © Derechos reservados del autor http://babel.banrepcultural.org/cdm/ref/collection/p17054coll23/id/488 |
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Biblioteca Virtual Banco de la República - Colecciones digitales |
collection |
Custom |
language |
Desconocido (Unknown) |
topic |
Bio-inspired machine learning; Gene expression programming; Hybrid behaviour; Co-evolution; Subsumption architecture Tecnología; Tecnología / Ingeniería y operaciones afines |
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Bio-inspired machine learning; Gene expression programming; Hybrid behaviour; Co-evolution; Subsumption architecture Tecnología; Tecnología / Ingeniería y operaciones afines Romero López, Oscar Javier Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents |
description |
In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Machine Learning System (MLS) based on bio-inspired techniques such as Extended Classifier Systems (XCS), Artificial Immune Systems (AIS), Neuro Connectionist Q-Learning (NQL) and Learning Classifier Systems (LCS) among others.
In this research an evolutionary mechanism based on Gene Expression Programming (GEP) to self-configure the behaviour arbitration between layers is suggested. In addition, a co-evolutionary mechanism to evolve behaviours in an independent and parallel fashion is used. The proposed approach was tested in an animat environment using a multi-agent platform and it exhibited several learning capabilities and emergent properties for self-configuring internal agent’s architecture. |
format |
Artículo (Article) |
author |
Romero López, Oscar Javier |
author_facet |
Romero López, Oscar Javier |
author_sort |
Romero López, Oscar Javier |
title |
Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents |
title_short |
Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents |
title_full |
Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents |
title_fullStr |
Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents |
title_full_unstemmed |
Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents |
title_sort |
multilayered evolutionary architecture for behaviour arbitration in cognitive agents |
publishDate |
2007 |
url |
http://babel.banrepcultural.org/cdm/ref/collection/p17054coll23/id/488 |
_version_ |
1682459964006727680 |
score |
12,131701 |