Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2010
Abstract
This paper presents a neural model that learns episodic traces in response to a continual stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatiotemporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Comparing with prior systems, the proposed episodic memory model presents a robust approach to encoding key events and episodes and recalling them using partial and erroneous cues. We present experimental studies, wherein the model is used to learn episodic memory of an agent’s experience in a first person game environment called Unreal Tournament. Our experimental results show that the model produces highly robust performance in encoding and recalling events and episodes even with incomplete and noisy cues.
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Proceedings of 2010 International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, Spain, July 18-23
First Page
447
Last Page
454
ISBN
9781424469178
Identifier
10.1109/IJCNN.2010.5596734
Publisher
IEEE
City or Country
New York
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.