From Function to Implementation: Exploring Degeneracy in Evolved Artificial Agents

Degeneracy—the ability of different structures to perform the same function—is a fundamental feature of biological systems, contributing to their robustness and evolvability. However, the ubiquity of degeneracy in systems generated through adaptive processes complicates our understanding of the behavioral and computational strategies they employ. In this study, we investigated degeneracy in simple computational agents, known as Markov brains, trained using an artificial evolution algorithm to solve a spatial navigation task with or without associative memory. We analyzed degeneracy at three levels: behavioral, structural, and computational, with a focus on the last. Using information-theoretical concepts, Tononi et al. (1999) proposed a functional measure of degeneracy within biological networks. Here, we extended this approach to compare degeneracy across multiple networks. Using information-theoretical tools and causal analysis, we explored the computational strategies of the evolved agents and quantified their computational degeneracy. Our findings reveal a hierarchy of degenerate solutions, from varied behaviors to diverse structures and computations. Even agents with identical evolved behaviors demonstrated different underlying structures and computations. These results underscore the pervasive nature of degeneracy in neural networks, blurring the lines between the algorithmic and implementation levels in adaptive systems, and highlight the importance of advanced analytical tools to understand their complex behaviors.