Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2012
Abstract
Memory enables past experiences to be remembered and acquired as useful knowledge to support decision making, especially when perception and computational resources are limited. This paper presents a neuropsychological- inspired dual memory model for agents, consisting of an episodic memory that records the agent's experience in real time and a semantic memory that captures factual knowledge through a parallel consolidation process. In addition, the model incorporates a natural forgetting mechanism that prevents memory overloading by removing transient memory traces. Our experimental study based on a real-time first-person-shooter video game has indicated that the memory consolidation and forgetting processes are not only able to extract valuable knowledge and regulate the memory capacity, but they can mutually improve the effectiveness of learning the knowledge for the given task in hand. Interestingly, a moderate level of forgetting may even improve the task performance rather than disadvantaging it. We suggest that the interplay between rapid memory formation, consolidation, and forgetting processes points to a practical and effective approach for learning agents to acquire and maintain useful knowledge from experiences in a scalable manner.
Keywords
Adaptive Resonance Theory, Forgetting, Memory
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012): Valencia, Spain, June 4-8
Volume
1
First Page
424
Last Page
431
Publisher
IFAAMAS
City or Country
Richland, SC
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.