Earthling logic for autonomous systems

Autonomous systems must function with minimal human intervention, requiring the ability to navigate complex environments and replicate human reasoning and decision-making across certainty, vagueness, and randomness. However, classical logic, fuzzy logic, and probability theory each address only isolated aspects of these domains. In particular, while fuzzy logic was designed to handle vagueness, it has several inherent limitations, such as arbitrary truth assignments, disregard for statement independence and correlation, and violations of fundamental logical principles like the law of noncontradiction and the law of the excluded middle, making it inconsistent with human intuition. In this paper, we introduce Earthling Logic (EL), a novel reasoning framework that closely mirrors human cognition by seamlessly integrating certainty, vagueness, and randomness. Mathematically rigorous and computationally efficient, EL effectively models human reasoning across these dimensions. Unlike classical logic, many-valued logic, and fuzzy logic, which rely on truth valuation, EL is fundamentally rooted in the benchmarks of truth and falsehood. By overcoming the limitations of existing logical frameworks, EL enhances autonomous systems’ reasoning and decision-making abilities in uncertain environments, enabling them to operate more effectively and naturally.