ADAPTIVE MACHINE INTELLIGENCE: THE UNIFIED ARCHITECTURE FOR CONTINUAL LEARNING, NEURO-SYMBOLIC REASONING, AND INTENT-AWARE TASK GENERALIZATION CREATING SELF-DIRECTED AI SYSTEMS INTEGRATING GOAL ABSTRACTION, MEMORY OPTIMIZATION, AND CROSS-DOMAIN SKILL

The evolution of machine intelligence toward truly autonomous, self-directed agents requires integrating diverse capabilities such as continual learning, symbolic reasoning, and generalization across novel environments. This paper proposes a unified architecture for adaptive machine intelligence, merging continual learning, neuro-symbolic reasoning, and intent-aware generalization. Our framework integrates goal abstraction with episodic and semantic memory optimization to facilitate skill transfer across domains, addressing the limitations of brittle, task-specific AI models. Drawing from literature, we synthesize foundational theories and recent progress in lifelong learning, neuro-symbolic computation, and hierarchical reinforcement learning. Experimental simulations in open-ended environments demonstrate emergent planning, adaptation, and semantic transfer capacities. We conclude with implications for AGI alignment and scalable cognitive systems.