Theoretical Robopsychology: Samu Has Learned Turing Machines
This work addresses theoretical questions in machine learning for developers and researchers, but it appears incremental as it builds on existing concepts like Turing machines and agent technology.
The paper tackles the problem of algorithms writing algorithms, specifically learning Turing machines, and concludes that this problem is difficult to handle in reality, making it reasonable to use machine learning algorithms for this task.
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.