Assessing Evolving and Learning-Based Controllers for Efficient Cursor Control in Human–Computer Interaction.

This work explores the use of evolving assistive controllers to improve user performance in target-pointing tasks within human-computer interaction. We investigate two assistance paradigms: (1) vectorial controllers that directly predict target-oriented displacement vectors (2) agent-based controllers trained via Reinforcement Learning. Each is implemented using either artificial gene regulatory networks or artificial neural networks. artificial gene regulatory networks were evolved using a genetic algorithm, while artificial neural network agents were optimized via backpropagation in the first case and a Soft Actor–Critic algorithm in the second case.