A Theoretical Model For Artificial Learning, Memory Management And Decision Making System

TL;DR

This paper proposes a new theoretical model for artificial learning, memory management, and decision making aimed at enabling machines to emulate human-like intelligence and complex behaviors.

Contribution

It introduces a novel theoretical framework that integrates learning, memory, and decision processes to advance human-like artificial intelligence systems.

Findings

Potential to develop systems with human-like decision making

Framework supports complex learning and memory management

Lays groundwork for future intelligent machine development

Abstract

Human beings are considered as the most intelligent species on Earth. The ability to think, to create, to innovate, are the key elements which make humans superior over other existing species on Earth. Machines lack all those elements, although machines are faster than human in aspects like computing, equating etc. But humans are still more valuable than machines, due to all those previously discussed elements. Various models have been developed in last few years to create models that can think like human beings, but are not completely successful. This paper presents a new theoretical system for learning, memory management and decision making that can be used to develop highly complex systems, and shows the potential to be used for development of systems that can be used to provide the essential features to the machines to act like human beings.