Journal of Pioneering Artificial Intelligence Research

Abstract

This study proposes a deterministic autonomous architecture based on layered admissibility filtering rather than unrestricted optimization or externally imposed goal assignment. In many practical robotic deployments, the most reliable autonomous systems may appear behaviorally conservative or even “boring” because persistence, safety, and operational continuity often dominate unrestricted exploratory behavior. Perception, sensing, semantic mapping, and world-modeling systems operate as structured input mechanisms that transform observed conditions into constraint sets governing admissible continuation states. The framework introduces an obligatory persistence layer in which operational survivability, energy availability, structural integrity, and maintenance constraints dominate early-stage admissibility filtering. Above this layer, role conditioned, contextual, and temporally dependent constraint structures progressively shape feasible goal space across immediate, medium-term, and long-term operational horizons. Admissible goals may therefore differ across autonomous agents operating within identical environments due to differences in persistence requirements, operational identity, and temporal continuity constraints. The resulting architecture supports bounded but non-rigid autonomous behavior while preserving deterministic operational coherence and compatibility with real-world robotic infrastructures. Multiple admissible continuation structures may coexist simultaneously provided they remain consistent with higher-order persistence and operational constraints. The framework intentionally avoids unrestricted autonomous objective generation, instead emphasizing persistence-compatible, role consistent, and temporally bounded autonomous continuity.