A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach

TL;DR

This paper introduces a heuristic search method for developing artificial general intelligence agents, leveraging the stability of learning algorithms as a fitness function, and explores its theoretical basis and potential links to fixed-point theorems.

Contribution

It proposes a novel heuristic search approach based on learning stability, aiming to improve AGI development and bridge AI with cognitive neuroscience.

Findings

Theoretical hypothesis on learning stability in specific scenarios.

Proposed correlation between stability and fixed-point theorem.

Preliminary discussion on the method's feasibility for AGI.

Abstract

This paper presents a non-manual design engineering method based on heuristic search algorithm to search for candidate agents in the solution space which formed by artificial intelligence agents modeled on the base of bionics.Compared with the artificial design method represented by meta-learning and the bionics method represented by the neural architecture chip,this method is more feasible for realizing artificial general intelligence,and it has a much better interaction with cognitive neuroscience;at the same time,the engineering method is based on the theoretical hypothesis that the final learning algorithm is stable in certain scenarios,and has generalization ability in various scenarios.The paper discusses the theory preliminarily and proposes the possible correlation between the theory and the fixed-point theorem in the field of mathematics.Limited by the author's knowledge level,this correlation is proposed only as a kind of conjecture.