Narrative Memory based Intelligent Agents

Abstract

Intelligent agents are increasingly being deployed in complex, dynamic environments where contextual understanding and adaptive decision-making are critical. This paper proposes a novel framework for intelligent agents that leverage narrative memory a structured, story-based representation of past experiences to enhance their ability to reason, learn, and act in dynamic scenarios. The framework integrates narrative theory with machine learning techniques to enable agents to construct, store, and retrieve narratives that capture temporal, causal, and contextual relationships. We evaluate the framework through a series of simulations and real-world case studies, demonstrating its effectiveness in improving decision-making, adaptability, and user interaction. The results suggest that narrative memory-based agents outperform traditional approaches in tasks requiring long-term context retention, causal reasoning, and explain ability. This research contributes to the advancement of intelligent systems by bridging the gap between computational efficiency and human-like narrative understanding.