A Cognitive Neuroscience Study in Multi-Agent Box-Pushing Adversarial Games

A Cognitive Neuroscience Study of Emergent Division of Labor, Altruistic Rescue, and Strategy Adaptation in Multi-Agent Box-Pushing Adversarial Games Based on Evolutionary Spiking Neural Networks and Online Plasticity English Version Abstract This study employs a co-evolutionary spiking neural network (SNN) model in a 20×10 grid box-pushing adversarial environment to systematically compare behavioral emergence under two conditions: fixed weights (evolution only) and R-STDP online learning. After multiple generations of evolutionary training, we recorded 1000-step behavioral data under both conditions. Under fixed weights : 42 pushes, 10 attacks (all executed by attack strategy), exploration rate 6.10%, 10 rescues (9 counter-kills, 1 teammate counter-attack), left team score ~500, right team 0, extreme division of labor in right team (Robot 1 pushes 21 times, Robot 0 pushes 0), left team division (Robot 1 pushes 20 times, Robot 0 pushes 1), multiple dodge-counter sequences, multiple bai