An ant-inspired model for multi-agent interaction networks without stigmergy

The aim of this work is to construct a microscopic model of multi-agent interaction networks inspired by foraging ants that do not use pheromone trails or stigmergic traces for communications. The heading and speed of each agent is influenced by direct interactions or encounters with other agents. Each agent moves in a plane using a correlated random walk whose probability distribution for heading change is made adaptable to these interactions and is superimposed with probability distributions that emulate how ants remember nest and food source locations. The speed of each agent is likewise influenced by a superposition of impetus and resistance effects that arise from its recent interactions. Additionally, the agents use a quorum sensing mechanism to trigger a non-deterministic decentralized congestion avoidance scheme. A discrete-time non-deterministic recruitment model is adopted and incorporated to regulate the population of foraging agents based on the amount of food perceived to exist in the environment. Simulation experiments were conducted to evaluate and demonstrate how agents employ the interaction network when foraging in open and closed environments as well as in scenarios with narrow pathways that trigger congestion.