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iConcept Journal of Human-Level Intelligence
iConcept Journal of Human-Level Intelligence
David Chik, Jitesh Dundas and Pei Wang
iConcept Press

iConcept Journal of Human-Level Intelligence

Implementing Human-like Intuition Mechanism in Artificial Intelligence

by Jitesh Dundas and David Chik

Volume: 4 (2014); Issue: 2
Status: Copy Editing and Typesetting


Human intuition has been simulated by several research projects using artificial intelligence techniques. Most of these algorithms or models lack the ability to handle complications or diversions. Moreover, they also do not explain the factors influencing intuition and the accuracy of the results from this process. In this paper, we present a simple series based model for implementation of human-like intuition using the principles of connectivity and unknown entities. By using Poker hand datasets and Car evaluation datasets, we compare the performance of some well-known models with our intuition model. The aim of the experiment was to predict the maximum accurate answers using intuition based models. We found that the presence of unknown entities, diversion from the current problem scenario, and identifying weakness without the normal logic based execution, greatly affects the reliability of the answers. Generally, the intuition based models cannot be a substitute for the logic based mechanisms in handling such problems. The intuition can only act as a support for an ongoing logic based model that processes all the steps in a sequential manner. However, when time and computational cost are very strict constraints, this intuition based model becomes extremely important and useful, because it can give a reasonably good performance. Factors affecting intuition are analyzed and interpreted through our model.

Author Details

Jitesh Dundas
Edencore Technologies, India
David Chik
Department of Brain Science and Engineering, Kyushu Institute of Technology, Japan

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