Foundations of a Developmental Design Paradigm for Integrated Continual Learning, Deliberative Behavior, and Comprehensibility

• URL: http://arxiv.org/abs/2502.13935v2
• Date: Sat, 18 Oct 2025 10:37:05 GMT
• Title: Foundations of a Developmental Design Paradigm for Integrated Continual Learning, Deliberative Behavior, and Comprehensibility
• Authors: Zeki Doruk Erden, Boi Faltings,
• Abstract summary: We introduce a system design that overcomes key limitations of current methods.<n>Our design comprises three core components: The Modeller, a gradient-free learning mechanism inherently capable of continual learning.<n>We extend our modeling framework to higher-dimensional network-structured spaces, using MNIST for a shape detection task.
• Score: 15.277175466744472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inherent limitations of contemporary machine learning systems in crucial areas -- importantly in continual learning, information reuse, comprehensibility, and integration with deliberate behavior -- are receiving increasing attention. To address these challenges, we introduce a system design, fueled by a novel learning approach conceptually grounded in principles of evolutionary developmental biology, that overcomes key limitations of current methods. Our design comprises three core components: The Modeller, a gradient-free learning mechanism inherently capable of continual learning and structural adaptation; a planner for goal-directed action over learned models; and a behavior encapsulation mechanism that can decompose complex behaviors into a hierarchical structure. We demonstrate proof-of-principle operation in a simple test environment. Additionally, we extend our modeling framework to higher-dimensional network-structured spaces, using MNIST for a shape detection task. Our framework shows promise in overcoming multiple major limitations of contemporary machine learning systems simultaneously and in an organic manner.

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