# On modelling the emergence of logical thinking

## TL;DR

This paper explores how to model the emergence of human-like logical thinking in artificial systems, emphasizing Piaget's cognitive development theory to go beyond current pattern recognition approaches.

## Contribution

It introduces a novel approach to simulate the development of logical thinking in AI based on Piaget's theory, aiming to bridge the gap to human-like reasoning.

## Key findings

- Highlights the limitations of current AI in understanding and reasoning.
- Proposes a developmental model inspired by Piaget's stages.
- Suggests pathways for future research in logical AI development.

## Abstract

Recent progress in machine learning techniques have revived interest in building artificial general intelligence using these particular tools. There has been a tremendous success in applying them for narrow intellectual tasks such as pattern recognition, natural language processing and playing Go. The latter application vastly outperforms the strongest human player in recent years. However, these tasks are formalized by people in such ways that it has become "easy" for automated recipes to find better solutions than humans do. In the sense of John Searle's Chinese Room Argument, the computer playing Go does not actually understand anything from the game. Thinking like a human mind requires to go beyond the curve fitting paradigm of current systems. There is a fundamental limit to what they can achieve currently as only very specific problem formalization can increase their performances in particular tasks. In this paper, we argue than one of the most important aspects of the human mind is its capacity for logical thinking, which gives rise to many intellectual expressions that differentiate us from animal brains. We propose to model the emergence of logical thinking based on Piaget's theory of cognitive development.