Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency

This research introduces new techniques for memory forgetting in autonomous AI agents, aiming to balance the relevance of stored information with computational efficiency. By selectively forgetting less important or outdated memories, agents can maintain focus on critical information, leading to more adaptive and efficient real-world applications in robotics, intelligent systems, and continuous learning environments.