Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IJCNN.2012.6252429 | IJCNN |
Keywords | Field | DocType |
semantic knowledge,procedural memory module,art,interconnected memory models,reinforcement learning,reactive response learning,fusion art network,fusion adaptive resonance theory network,autonomous agents,semantic memory,learning (artificial intelligence),unreal tournament,episodic memory,art neural nets,declarative knowledge learning,multi-robot systems,independent memory models,declarative memory module,agent,self-organizing,knowledge transfer,multi-agent systems,self-organizing multimemory system,procedural knowledge learning,self-organising feature maps,procedural memory,computational modeling,vectors,semantics,multi agent systems,learning artificial intelligence | Procedural knowledge,Semantic memory,Episodic memory,Descriptive knowledge,Autonomous agent,Procedural memory,Computer science,Multi-agent system,Artificial intelligence,Machine learning,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
2161-4393 E-ISBN : 978-1-4673-1489-3 | 978-1-4673-1489-3 | 2 |
PageRank | References | Authors |
0.46 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenwen Wang | 1 | 46 | 3.61 |
| Budhitama Subagdja | 2 | 90 | 13.41 |
| Ah-Hwee Tan | 3 | 1385 | 112.07 |
| Yuan Sin Tan | 4 | 36 | 5.74 |