A self-organizing multi-memory system for autonomous agents

• Main Authors: Wang, Wenwen, Subagdja, Budhitama, Tan, Ah-Hwee, Tan, Yuan-Sin
• 其他作者: School of Computer Engineering
• 格式: Conference or Workshop Item
• 語言: English
• 出版: 2013
• 主題: DRNTU::Engineering::Computer science and engineering
• 在線閱讀: https://hdl.handle.net/10356/97847; http://hdl.handle.net/10220/12402

This paper presents a self-organizing approach to the learning of procedural and declarative knowledge in parallel using independent but interconnected memory models. The proposed system, employing fusion Adaptive Resonance Theory (fusion ART) network as a building block, consists of a declarative memory module, that learns both episodic traces and semantic knowledge in real time, as well as a procedural memory module that learns reactive responses to its environment through reinforcement learning. More importantly, the proposed multi-memory system demonstrates how the various memory modules transfer knowledge and cooperate with each other for a higher overall performance. We present experimental studies, wherein the proposed system is tasked to learn the procedural and declarative knowledge for an autonomous agent playing in a first person game environment called Unreal Tournament. Our experimental results show that the multi-memory system is able to enhance the performance of the agent in a real time environment by utilizing both its procedural and declarative knowledge.