Abstract Mathematical Algorithms and Valuations for the Construction of Artificial General Intelligence (AGI) and Generalized Problem Solving

This research explores the development of advanced mathematical algorithms aimed at generating functions for Artificial General Intelligence (AGI). By integrating generalized reinforcement learning valuation constructions with abstract mathematical structures, the study establishes a comprehensive theoretical framework for autonomous complex problem solving. Key innovations include hyperaction and hyperobservation mappings, generalized state sets, levels of abstraction, downwards lattice structuring and upwards expanding transducer confluence classes. These components enable the system to manage complex transformations and actions across various levels of abstraction. Leveraging recent advancements in reinforcement learning, this work seeks to advance the field of AGI by creating robust algorithms capable of performing tasks with a level of intelligence comparable to human cognition. The interdisciplinary approach, combining abstract algebraic structures with cutting-edge AI research, marks a significant step towards realizing fully autonomous intelligent systems.