An Improved Intelligent Agent for Mining Real-Time Databases Using Modified Cortical Learning Algorithms

TL;DR

This paper introduces an enhanced intelligent agent framework utilizing modified Cortical Learning Algorithms to improve real-time data mining and recognition in synthetic and real datasets.

Contribution

It presents a novel application of modified HTM-based algorithms for real-time data mining, advancing the development of intelligent agents.

Findings

Improved recognition accuracy demonstrated on datasets

Effective real-time data mining achieved

Modified algorithms outperform existing methods

Abstract

Cortical Learning Algorithms based on the Hierarchical Temporal Memory, HTM have been developed by Numenta Incorporation from which variations and modifications are currently being investigated upon. HTM offers better promises as a future computational model of the neocortex the seat of intelligence in the brain. Currently, intelligent agents are embedded in almost every modern day electronic system found in homes, offices and industries worldwide. In this paper, we present a first step in realising useful HTM like applications specifically for mining a synthetic and real time dataset based on a novel intelligent agent framework, and demonstrate how a modified version of this very important computational technique will lead to improved recognition.