Theoretical Robopsychology: Samu Has Learned Turing Machines

TL;DR

This paper explores the theoretical aspects of robopsychology focusing on Turing machines, proposing the use of machine learning algorithms to learn and understand Turing machines due to the complexity of direct learning.

Contribution

It introduces the problem of learning Turing machines and advocates for applying machine learning algorithms to address this challenge, linking theory with practical approaches.

Findings

Learning Turing machines is complex and not easily handled directly.

Machine learning algorithms are suitable for learning and understanding Turing machines.

The approach combines pedagogical methods with agent technology and machine learning.

Abstract

From the point of view of a programmer, the robopsychology is a synonym for the activity is done by developers to implement their machine learning applications. This robopsychological approach raises some fundamental theoretical questions of machine learning. Our discussion of these questions is constrained to Turing machines. Alan Turing had given an algorithm (aka the Turing Machine) to describe algorithms. If it has been applied to describe itself then this brings us to Turing's notion of the universal machine. In the present paper, we investigate algorithms to write algorithms. From a pedagogy point of view, this way of writing programs can be considered as a combination of learning by listening and learning by doing due to it is based on applying agent technology and machine learning. As the main result we introduce the problem of learning and then we show that it cannot easily be handled in reality therefore it is reasonable to use machine learning algorithm for learning Turing machines.