Adaptive Memory Retrieval Algorithms for Long-Term Context Retention in Autonomous Agents

Abstract

Sequential decision-making, learning, and adaptation depend on the use of memory mechanisms in autonomous agents. In dynamic environments, traditional fixed or heuristic memory models do not retain relevant historical information for a long time, making agents' performance limited. The authors present an adaptive memory retrieval mechanism that is able to effectively capture, store and retrieve long-term context, leading to improved decision making and computational efficiency for autonomous agents. Experiences are represented as vectors, with context information that represents states, actions, and outcomes. The relevance weighted retrieval mechanism dynamically scores, ranks, and retrieves stored memories based on their similarity with the current state of the system and their historical importance. The memories with high relevance will be retrieved and used in making the decisions for the agent, and the low-relevance memories will be pruned to optimize the computational cost. The framework is formalised through relevance-cost optimization function and tested on benchmark agent trajectory datasets of different memory buffer sizes. The experimental findings show that the adaptive approach gives a retrieval accuracy of 92.4%, context relevancy of 88.7% and decision making success of 89.7%, outperforming the fixed memory and heuristic approaches whose accuracy is 78.9% and 84.5% respectively. The average retrieval time is 22 ms, hence proving the computational efficiency. The qualitative analysis shows that decision-making that is context-aware improves while validating the use of past experience. The proposed approach is efficient and flexible in managing the long-term memory of autonomous agents.  Future work can investigate real-time adaptation, multi-agent deployment, and integration with reinforcement learning or heterogeneous agent networks to further improve the retention of context and agent performance.