Continual Developmental Neurosimulation Using Embodied Computational Agents
This work addresses the challenge of synthesizing developmental biology and AI for researchers in computational modeling and cognitive science, but it is incremental as it builds on existing Braitenberg Vehicle concepts without presenting new empirical data.
The paper tackles the problem of creating developmentally-inspired learning agents by proposing a framework using developmental Braitenberg Vehicles (dBVs) that transform from undefined structures into embodied systems with nervous systems, aiming to integrate morphogenesis, learning, and plasticity. The result is a conceptual approach that exploits critical periods and embodied architectures for adaptive behavior, though no concrete numerical results are provided.
There is much to learn through synthesis of Developmental Biology, Cognitive Science and Computational Modeling. Our path forward involves a design for developmentally-inspired learning agents based on Braitenberg Vehicles. Continual developmental neurosimulation allows us to consider the role of developmental trajectories in bridging the related phenomena of nervous system morphogenesis, developmental learning, and plasticity. Being closely tied to continual learning, our approach is tightly integrated with developmental embodiment, and can be implemented using a type of agent called developmental Braitenberg Vehicles (dBVs). dBVs begin their lives as a set of undefined structures that transform into agent-based systems including a body, sensors, effectors, and nervous system. This phenotype is characterized in terms of developmental timing: with distinct morphogenetic, critical, and acquisition (developmental learning) periods. We further propose that network morphogenesis can be accomplished using a genetic algorithmic approach, while developmental learning can be implemented using a number of computational methodologies. This approach provides a framework for adaptive agent behavior that might result from a developmental approach: namely by exploiting critical periods or growth and acquisition, an explicitly embodied network architecture, and a distinction between the assembly of neuronal networks and active learning on these networks. In conclusion, we will consider agent learning and development at different timescales, from very short (<100ms) intervals to long-term evolution. The development, evolution, and learning in an embodied agent-based approach is key to an integrative view of biologically-inspired intelligence.