Learning processes, cognitive architectures, available resources, and methods for sampling the environment and generating intelligent responses in complex sensory domains can differ significantly between natural and artificial systems. In this work, we present theoretical and modeling-based analysis of early-stage learning under resource constraints, comparing biological intelligence with a class of freely evolving, weakly constrained artificial systems (FEW), focusing on essential resource cons
Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems
Serhii Podelskyi
