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A Subsumption Architecture to Develop Dynamic Cognitive Network-Based Models With Autonomous Navigation Application

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Abstract

This work proposes an original approach based on subsumption architecture to build dynamic cognitive networks (DCN). Dynamic cognitive network is a soft computing technique similar to fuzzy cognitive maps (FCM) which are able to easily model cause–effect behaviors. FCMs have been applied in several areas of knowledge; however, they present some restrictions for modeling dynamic systems, specifically temporal dependencies among events. Due to these restrictions, alternative approaches based on FCM and also fuzzy networks have appeared in the literature. Dynamic cognitive networks are one of these techniques. Hence, this study presents a new type of DCN which incorporates different types of concepts and causal relations able to circumvent the main drawbacks of FCM modeling. The new approach is based on the subsumption architecture, which allows to represent, model, and implement several behaviors of a dynamic systems through a composition of hierarchical DCNs. An application of the new DCN technique in autonomous navigation is also developed in order to validate the approach.

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Acknowledgments

Financial support of the Brazilian Petroleum Agency (ANP) and the Research and Projects Financing Agency (FINEP) through Grant PRH-ANP / MCT:PRH10-UTFPR; and Brazilian Research Council (CNPq) through Grants 304037/2010-9 and 311877/2009-5 is acknowledged.

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Authors and Affiliations

  1. Universidade Tecnológica Federal do Paraná, Cornélio Procópio, Paraná, Brasil

    Márcio Mendonça & Bruno Augusto Angélico

  2. Universidade Tecnológica Federal do Paraná, Curitiba, Paraná, Brasil

    Lúcia Valéria Ramos de Arruda & Flávio Neves Jr

Authors
  1. Márcio Mendonça
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  2. Bruno Augusto Angélico
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  3. Lúcia Valéria Ramos de Arruda
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  4. Flávio Neves Jr
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Correspondence to Lúcia Valéria Ramos de Arruda.

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Mendonça, M., Angélico, B.A., de Arruda, L.V.R. et al. A Subsumption Architecture to Develop Dynamic Cognitive Network-Based Models With Autonomous Navigation Application. J Control Autom Electr Syst 24, 117–128 (2013). https://doi.org/10.1007/s40313-013-0008-3

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  • DOI: https://doi.org/10.1007/s40313-013-0008-3

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