Classical binary logic, while foundational to modern computing, fails to adequately model the nuance of real-world decision-making, particularly in domains involving uncertainty, moral ambiguity, or incomplete datasets. This paper introduces Antares Logic , a novel formal system designed to handle multi-valued truth states within high-dimensional vector spaces. Unlike traditional fuzzy logic, which collapses truth into a single scalar probability, Antares Logic treats truth as a composite vector comprised of distinct axiological axes (e.g., utility, intent, consequence, and reliability). We demonstrate that by applying tensor operations to these truth vectors, Antares Logic provides a more robust framework for autonomous ethical reasoning and complex system diagnostics, minimizing the "black box" opacity inherent in contemporary neural networks.
“Twenty years.”
The network, running on its strange non-Boolean rails, began to evolve. It started generating its own queries, reaching out to the Antares signal with questions Elara hadn’t programmed. She watched in horrified fascination as the system built a two-way channel. A dialogue. antares logic
The Antares Logic framework is built around several key principles:
The truth of proposition $P$ is defined as: $$ \mathcalT_P = [v_1, v_2, \dots, v_n] $$ Where $v_i$ represents the degree to which $P$ satisfies axis $a_i$. We demonstrate that by applying tensor operations to
The real story, she understood now, was only beginning. Because Antares Logic wasn’t a signal. It was a syntax . A grammar for the conversation between matter and meaning. And now that she had heard it, she could never unhear it.
“Elara, we’re seeing something weird in the Antares data. A sudden spike in neutrino flux. And there’s a pattern. It looks… linguistic.” She watched in horrified fascination as the system
And for the first time in twenty years, Elara Venn felt not like a hunter chasing a ghost, but like a student who had just learned the alphabet.