A Cognitive Framework for Memory Reconstruction in Artificial Systems

Artificial cognitive systems suitable of storing and recalling complex patterns are vital for advancing independent intelligence. These systems bear the integration of episodic and semantic memory structures to reconstruct shattered information without significant interference. This study presents a new frame for artificial memory reconstruction inspired by mortal cognitive processes. The frame includes the storage and recovery of spatio-temporal patterns and the operation of Intelligent Software Agents to emulate mortal- suchlike memory functionality. By using contextual integration and similarity- predicated generality, this architecture achieves adaptive memory reconstruction and robust information operation. The proposed methodology highlights a scalable and effective approach to memory systems in artificial intelligence.