Lateralized Learning to Solve Complex Boolean Problems

doi:10.1109/TCYB.2022.3166119

2023-11-01

Abubakar Siddique *

Will N. Browne

Gina M. Grimshaw

DOI：10.1109/TCYB.2022.3166119

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摘要

En 中文

Modern classifier systems can effectively classify targets that consist of simple patterns. However, they can fail to detect hierarchical patterns of features that exist in many real-world problems, such as understanding speech or recognizing object ontologies. Biological nervous systems have the ability to abstract knowledge from simple and small-scale problems in order to then apply it to resolve more complex problems in similar and related domains. It is thought that lateral asymmetry of biological brains allows modular learning to occur at different levels of abstraction, which can then be transferred between tasks. This work develops a novel evolutionary machine-learning (EML) system that incorporates lateralization and modular learning at different levels of abstraction. The results of analyzable Boolean tasks show that the lateralized system has the ability to encapsulate underlying knowledge patterns in the form of building blocks of knowledge (BBK). Lateralized abstraction transforms complex problems into simple ones by reusing general patterns (e.g., any parity problem becomes a sequence of the 2-bit parity problem). By enabling abstraction in evolutionary computation, the lateralized system is able to identify complex patterns (e.g., in hierarchical multiplexer (HMux) problems) better than existing systems.

Keyword:

Task analysis

Multiplexing

Face recognition

Computer architecture

Technological innovation

Speech recognition

Organizations

Building blocks

cognitive neuroscience

lateralization

learning classifier systems (LCSs)