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CBR based reactive behavior learning for the memory-prediction framework
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DOI：10.1016/j.neucom.2016.10.075.png)
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摘要
En 中文
Some approaches to intelligence state that the brain works as a memory system which stores experiences to reflect the structure of the world in a hierarchical, organized way. Case Based Reasoning (CBR) is well suited to test this view. In this work we propose a CBR based learning methodology to build a set of nested behaviors in a bottom up architecture. To cope with complexity-related CBR scalability problems, we propose a new 2-stage retrieval process. We have tested our framework by training a set of cooperative/competitive reactive behaviors for Aibo robots in a RoboCup environment. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Case based reasoning
Reactive behaviors
Behavior learning
Robotics
Control architecture
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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