Name
Abstract | ||
|---|---|---|
Biological nervous systems can learn knowledge from simple and small-scale problems and then apply it to resolve more complex and large-scale problems in similar and related domains. However, the rudimentary attempts to apply this transfer learning in artificial intelligence systems have struggled. This is maybe due to the homogeneous nature of their knowledge representation. It is believed that it is the lateral asymmetry of the brain, enabling modular learning at different levels of abstraction, which facilitates transfer between tasks. Learning classifier systems (LCSs) are a rule-based evolutionary computation technique that automatically clusters inputs into environmental niches, which makes it an ideal candidate for implementing lateralization.
Recently LCSs based systems have applied lateralization and modular learning at different levels of abstraction to solve complex problems in Boolean, computer vision, and navigation domains. This paper aims to bring these three separate implementations together for the first time to understand the methodology of lateralization and appreciate its benefits. The experimental results demonstrate that the LCSs based lateralized systems outperformed state-of-the-art homogeneous systems in solving complex problems. The advances arise from the ability to consider input at both the constituent level and holistic level simultaneously, such that the most appropriate viewpoint controls the system.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3377929.3398101 | GECCO '20: Genetic and Evolutionary Computation Conference
Cancún
Mexico
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7127-8 | 1 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abubakar Siddique | 1 | 2 | 0.72 |
| Will N. Browne | 2 | 844 | 50.87 |
| Gina M. Grimshaw | 3 | 3 | 2.09 |