A NEURO-SYMBOLIC FRAMEWORK FOR AUTONOMOUS ETHICAL REASONING AND INTENT-AWARE ADAPTATION TOWARD CONSCIOUS AGENTS INTEGRATING META-COGNITION, GOAL SHAPING, AND CONTEXTUAL REASONING IN NEXT-GENERATION AI SYSTEMS

The pursuit of artificial consciousness demands frameworks that can go beyond rule-following to incorporate ethical reasoning, self-awareness, and adaptive decision-making. This paper presents a novel neuro-symbolic framework that integrates meta-cognition, goal shaping, and contextual reasoning to enable ethical adaptation in intelligent systems. Combining deep neural learning with symbolic logic processing, the proposed architecture supports intent-aware, context-driven decisions critical to future conscious agents. The framework enables dynamic ethical evaluation, introspective feedback loops, and real-time adaptation to environmental and internal states. A key focus lies in aligning machine goals with human-compatible value systems using meta-cognitive signaling and structured moral ontologies. Results demonstrate improved ethical responsiveness and goal conflict resolution under uncertain and multi-agent environments. The study bridges symbolic moral reasoning with neural adaptability, setting the foundation for safe, explainable, and ethically-aligned artificial agents. Experimental simulations validate the framework’s capacity to operate in high-dimensional, evolving environments, supporting progress toward responsible next-generation AI.