A self-organizing multi-memory system for autonomous agents

• Main Authors: WANG, Wenwen, SUBAGDJA, Budhitama, TAN, Ah-hwee, TAN, Yuan-Sin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: agent; ART; episodic memory; procedural memory; self-organizing; semantic memory; Unreal Tournament; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6165; https://ink.library.smu.edu.sg/context/sis_research/article/7168/viewcontent/6887719.pdf
• Institution: Singapore Management University

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.