Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
This work addresses the challenge of enabling autonomous learning in changing environments for AI systems, though it is incremental as it builds on existing Hebbian and evolutionary approaches.
The paper tackled the problem of discovering interpretable local Hebbian learning rules for autonomous global learning in artificial neural networks, using genetic algorithms to evolve rules that achieved performance comparable to offline methods like hill climbing in foraging and prey-predator tasks.
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.