CBR based reactive behavior learning for the memory-prediction framework

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.