Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.10.075 | Neurocomputing |
Keywords | Field | DocType |
Case based reasoning,Reactive behaviors,Behavior learning,Robotics,Control architecture | Architecture,Computer science,Top-down and bottom-up design,AIBO,Artificial intelligence,Robot,Case-based reasoning,Memory-prediction framework,Robotics,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
250 | C | 0925-2312 |
Citations | PageRank | References |
1 | 0.38 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| I. Herrero-Reder | 1 | 1 | 0.38 |
| C. Urdiales | 2 | 251 | 33.14 |
| Jose Manuel Peula | 3 | 27 | 5.49 |
| Francisco Sandoval Hernández | 4 | 771 | 104.15 |