Learning Clasiffier Systems with Hebbian Learning for Autonomus Behaviors

One of the main characteristics of multi-agent systems is the ability to solve problems achieving objectives. This is possible because of the learning mechanisms that are embedded in the systems and go from neural networks up to vector support machines. Agent-based systems stand out for their autonomy and adaptation of dynamic conditions of the environment. This article presents the Hebbian theory, which is one of the learning methods from the neuroscience field. A particularity presented by the Hebbian theory from the computer since field perspective is the primary mechanism of synaptic plasticity where the value of a synaptic connection increases if neurons on both sides of a said synapse are activated repeatedly, creating a new one simultaneously. This mechanism is integrated into the Learning Classifier Systems (LCS) to validate its effectiveness in the solution task, and can be used in multi-agent systems.