Name
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 spatio-temporal 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596734 | IJCNN |
Keywords | Field | DocType |
self-organizing approach,fusion adaptive resonance theory,episodic memory modeling,fusion art network,cognitive nodes,art neural nets,memory search procedure,unreal tournament,stored episodic traces,cognitive systems,search problems,game theory,game environment,continual stream,sensory input,sensory feedback,neural model,parallel search,spatio-temporal relations,games,encoding,computational modeling,self organization,adaptive resonance theory,artificial neural networks,episodic memory,noise measurement | Episodic memory,Adaptive resonance theory,Computer science,Parallel search,Search procedure,Game theory,Artificial intelligence,Cognition,Artificial neural network,Machine learning,Encoding (memory) | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 10 |
PageRank | References | Authors |
0.72 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenwen Wang | 1 | 46 | 3.61 |
| Budhitama Subagdja | 2 | 90 | 13.41 |
| Ah-Hwee Tan | 3 | 1385 | 112.07 |
| Janusz A. Starzyk | 4 | 440 | 36.95 |