Simulating Human Innovation for AGI Development

Abstract:Artificial General Intelligence (AGI) aspires to replicate human-level reasoning and learning across diverse domains. Yet, most current AI systems fall short of understanding the dynamic, goal-driven, and iterative processes that characterize human innovation. This paper proposes a novel framework for AGI development by simulating the cognitive journey of inventors—using Thomas Edison’s path to the lightbulb as a case study. The approach models not just outcomes, but the underlying behaviors that drive innovation, including trial-and-error, strategic shifts, and persistent adaptation. We introduce a five-module architecture comprising a Goal Simulator, Iteration Engine, Strategy Switcher, Memory Engine, and Pattern Extractor. This simulation-based model aims to foster problem-solving abilities in AI that more closely resemble human creativity and reasoning. By incorporating insights from reinforcement learning and cognitive science, the framework can be extended to build more adaptable, general-purpose intelligent systems.