Distributed Control Multi-Agent Systems

Closed-loop real-virtual interactions validate 3D model of social coordination in fish

The paper develops a 3D model of social coordination in fish using real behavior data and validates it through a closed-loop virtual reality system.

Swimming speed of schooling fish controls social interaction strength in open-loop immersive virtual reality

This study shows that swimming speed affects social interaction strength in fish by altering attraction, alignment, and repulsion behaviors in a virtual reality setup.

Group size and social interactions' strength determine collective response to perturbation in a model of burst-and-coast swimming fish

The paper uses a computational model to show that group size and social interaction strength affect how fish groups respond to perturbations, with larger groups being more sensitive to disturbances.

Towards a mechanistic understanding of collective escape in starlings

This study investigates collective escape behavior in starlings using video data and a 3D agent-based model, identifying individual-level rules that generate flock dynamics similar to real starlings.

Pairwise interactions, feedback rule changes, and deliberative decisions underlie honeybee inflight group coordination

This paper analyzes honeybee flight coordination in crowded conditions, identifying a three-stage decision-making process involving feedback rule changes and velocity regulation.

Allocentric Flocking

This paper introduces an allocentric framework for modeling collective motion, showing that coherent group behavior can emerge from navigational circuits without explicit alignment rules.

Experimental evidence of stress-induced critical state in schooling fish

This study shows that schooling fish adjust their social interactions under stress to reach a critical state, enhancing adaptability, with group size influencing the need for external stressors.

Collective phases and long-term dynamics in a fish school model with burst-and-coast swimming

This paper introduces a fish school model with burst-and-coast swimming that replicates collective phases like schooling and swarming, but cohesion diminishes over time except in swarming.

Collective Evolution Learning Model for Vision-Based Collective Motion with Collision Avoidance

Q-learning policy trained on genetic-algorithm targets lets simulated locust-shaped agents with only visual input flock (CM score 0.7) and avoid collisions (CA 0.74 vs 0.17 for a flocking-only policy).