Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2012

Abstract

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.

Keywords

agent, ART, episodic memory, procedural memory, self-organizing, semantic memory, Unreal Tournament

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Brisbane, Australia, June 10-15

First Page

480

Last Page

487

ISBN

9781467314909

Identifier

10.1109/IJCNN.2012.6252429

Publisher

IEEE

City or Country

New York

Download

Share

COinS