Recursion and evolution: Part II
This work addresses a theoretical problem in AI and cognitive systems, but it appears incremental as it builds on existing concepts without clear practical application.
The paper investigates whether a diagonalizing system can learn to adapt by using environmental reward and punishment as information, specifically focusing on learning diagonalization based on a rewarding function and exploring related memory phenomena.
We examine the question of whether it is possible for a diagonalizing system, to learn to use environmental reward and punishment as an information, in order to appropriately adapt. More specifically, we study the possiblity of such a system, to learn to use diagonalization on the basis of a rewarding function. Relevant phenomena regarding memory are also investigated.