Steps toward Artificial General Intelligence: A Mathematical Encoding of Synthetic Intelligence

Source abstract

Artificial general intelligence (AGI) represents an ambitious goal in the field of AI aiming to replicate the versatile cognitive abilities of the human mind. This chapter presents a mathematical framework to encode synthetic intelligence as a progression toward achieving AGI capabilities. The research establishes a novel formalism that encodes essential properties of behavioral intelligence. This formalism is derived through concepts of multidimensional wave representations and mass-action interpretation of Nash equilibrium, modeled through interactions in game theoretic spaces. This mathematical abstraction encapsulates intelligent thought processes through frame systems and neural architectures, while strategic decision-making emerges from stigmergic information flow in non-cooperative population dynamics. Significant mathematical rigor is achieved in formulating this architecture by integrating strategic equilibria at multiple scales, from individual cognitive elements to collective behavioral emergence. Through mathematical encoding of behavioral intelligence using the mass-action interpretation of Nash equilibrium within large populations of non-cooperative, pseudo-intelligent agents, this research provides an interpretable formalization building toward AGI. The framework demonstrates potential applications across autonomous systems, swarm robotics, and distributed AI architectures, particularly in strategic decision-making and collective intelligence emergence. The proposed formalism extends beyond traditional AI approaches by modeling intelligence as manipulable multidimensional wave spaces, providing a theoretically grounded pathway to strong AI capabilities in machines through the integration of game theory, neural architectures, and population dynamics.