Evolution Theory of Self-Evolving Autonomous Problem Solving Systems
This work addresses foundational challenges in AI for developing self-evolving systems, but appears incremental as it builds on existing mathematical concepts.
The study proposes a mathematical framework for self-evolving autonomous problem solving systems, focusing on universal abstraction and decidability of solutions, establishing a new structure through saturation and iterative closures.
The present study gives a mathematical framework for self-evolution within autonomous problem solving systems. Special attention is set on universal abstraction, thereof generation by net block homomorphism, consequently multiple order solving systems and the overall decidability of the set of the solutions. By overlapping presentation of nets new abstraction relation among nets is formulated alongside with consequent alphabetical net block renetting system proportional to normal forms of renetting systems regarding the operational power. A new structure in self-evolving problem solving is established via saturation by groups of equivalence relations and iterative closures of generated quotient transducer algebras over the whole evolution.