A Cognitive Framework for Memory Reconstruction in Artificial Systems

Abstract

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.

Introduction

Memory is a fundamental component of intelligent systems, enabling the recall and use of past information. Human memory consists of episodic memory (specific events) and semantic memory (general knowledge), inspiring artificial memory systems that aim to reconstruct memories from partial or noisy data. Recent research focuses on creating artificial cognitive architectures that efficiently store, categorize, and retrieve fragmented information to support reasoning and learning in dynamic environments.

The paper introduces the ISAAC Artificial Cognitive Neural Framework (ACNF), a hybrid system combining neural networks, genetic algorithms, fuzzy logic, and complex components to mimic human-like memory functions. The ACNF processes heterogeneous and incomplete data through recombinant knowledge assimilation, enabling adaptive decision-making and robust memory reconstruction.

ISAAC’s memory system is based on constructivist learning, organizing information into short-term, long-term, and emotional memories, represented as interconnected fragments rather than static databases. It uses advanced encoding techniques like Binary Information Fragments (BIFs) and employs various neural vector types to reconstruct memories from episodic, semantic, and emotional inputs.

Challenges include handling noisy data, scalability, computational demands, and ethical concerns, especially in sensitive fields like healthcare. Future work aims to improve learning precision, integrate IoT for real-time data, leverage quantum computing for performance, and develop domain-specific adaptations with transparent AI for responsible deployment.

Conclusion

This study proposes a comprehensive frame for memory encoding, storehouse, and reconstruction in artificial cognitive systems, inspired by mortal memory processes. By exercising spatio-temporal patterns, intelligent agents, and dynamic knowledge representation, the system effectively processes, encodes, and retrieves fractured information. The integration of episodic and semantic memory capabilities ensures rigidity to real- world complications and dynamic scripts. Although promising advancements have been demonstrated, further confirmation and refinement are needed to enable practical operations. unborn exploration will prioritize developing simplified prototypes for testing and assessing their effectiveness in disciplines similar as robotics, healthcare, and decision- support systems. Enhancing processing pets, reducing resource demands, and mollifying hindrance during memory reclamation are critical areas of enhancement. Ethical considerations will also play a central part in guiding the responsible design and deployment of these systems. This frame establishes a foundational step toward the consummation of artificial memory systems, bridging the gap between abstract fabrics and functional systems. Sustained invention and rigorous evaluation will be essential to achieving further independent, effective, and intelligent cognitive infrastructures able of addressing complex challenges across different fields.

References

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Copyright

Copyright © 2025 Mrs. Divya P, Karthika K S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.