Presentation of ACT/R-RBF Hybrid Architecture to Develop Decision Making in Continuous and Non-continuous Data

Nader Rezazadeh,Touraj Banirostam

doi:10.1515/jisys-2017-0470

Nader Rezazadeh, Touraj Banirostam

Open Access

https://doi.org/10.1515/jisys-2017-0470

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Abstract

Abstract Computational models are based on symbolic architecture. For this reason, computational models function problematically in dynamic, noisy, and continuous environments. The ACT/R (Adaptive Control of Thought-Rational) model is also problematic, as it is purely based on symbolic architecture like other computational models. The ACT/R decision-making process is based on the production operator on the input subject set. This approach firstly does not make a non-linear mapping between input and the decision-making result in ACT/R. Secondly, it is not possible to decide on the input subjects with a continuous input range because of the need to introduce numerous rules. The objective of presenting the ACT/R-radial basis function (RBF) hybrid architecture method was to create a communication network between input concepts in which the reception of and decision making on a combination of subjects and symbols are possible. Moreover, a non-linear mapping between input and the decision-making result can be created. The said capabilities have been obtained by the combination of ACT/R with an RBF neural network and calculation of the decision-making centers in the said network using clustering. The empirical experiments indicate desirable results in this regard.

Highlights

Decision making is a cognitive process in the mind whose result is to select an alternative among all alternatives based on the available conditions [1]
If the input is determined for each of the sentences saved at the range of distance, the decision making is directly done by the ACT/R model and the procedural knowledge module
It has been tried to use a connectional model in combination with the ACT/R model to improve the performance of the ACT/R model

Summary

Introduction

Decision making is a cognitive process in the mind whose result is to select an alternative among all alternatives based on the available conditions [1]. One of the advantages of using cognitive architecture in decision-making processes is to investigate all possible alternatives for the elements on which decision making is done [20]. This is possible by relying on the interaction of the knowledge specified in the cognitive model and declarative knowledge [26]. The main problem of the approaches that are based on rational models and decisionmaking principles is ignoring the decision-making cognitive abilities. Most of the studies have focused on dimension reduction and extracting high-level features while neglecting the basic cognitive aspect of decision making [17]

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