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Sujatha Sujatha
Bharathi Women’s College (Autonomous)
Computer Science & IT Electrical & Electronics Engineering

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Symbolic-Connectionist Representational Model for Optimizing Decision Making Behavior in Intelligent Systems R. John Martin; Sujatha Sujatha
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (267.525 KB) | DOI: 10.11591/ijece.v8i1.pp326-332

Abstract

Modeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients.
Co-Authors R. John Martin