We introduce a simulation environment to facilitate research into emergent collective behaviour, with a focus on replicating the dynamics of ant colonies. By leveraging real-world data, the environment simulates a target ant trail that a controllable agent must learn to replicate, using sensory data observed by the target ant. This work aims to contribute to the neuroevolution of models for collective behaviour, focusing on evolving neural architectures that encode domain-specific behaviours in the network topology. By evolving models that can be modified and studied in a controlled environment, we can uncover the necessary conditions required for collective behaviours to emerge. We hope this environment will be useful to those studying the role of interactions in emergent behaviour within collective systems.

This study applies the information-theoretic measure of Non- Trivial Information Closure (NTIC) to quantify the autonomy of individual ants within a colony. We calculate the degree to which an ant’s future behavior is determined by its own past states versus being influenced by its local environment. Results show that individual ants exhibit consistent levels of autonomy across different timescales. This suggests that ant behavior reflects a non-trivial processing of both internal and external information, rather than being a simple reflexive response to stimuli. The approach demonstrates the utility of NTIC as a metric for assessing autonomy in complex biological systems. These findings lay the groundwork for future studies of autonomy and information processing in swarms.

We conducted a comprehensive tracking study of 64 unmarked ants within the same arena to examine the dynamics of individual behaviors within a collective, aiming to understand the underlying mechanisms that drive the colony's collective behaviors. Specifically, we analyzed the movement patterns of the ants to identify the “algorithm” governing their actions. One such approach we employed is the ϵ -machine method, pioneered by Crutchfield and colleagues, which predicts motion using a stochastic finite state machine. The results of our study revealed that individual ants exhibited either deterministic or stochastic behaviors, contingent upon their roles within the colony. Ants contributing to cluster formation displayed deterministic behaviors, whereas those exploring outside of the cluster were more likely to demonstrate stochastic behaviors.

A decentralised collective learning problem is investigated in which a population of agents attempts to learn the true state of the world based on direct evidence from the environment and belief fusion carried out during local interactions between agents. A parameterised fusion operator is introduced that returns beliefs of varying levels of imprecision. This is used to explore the effect of fusion imprecision on learning performance in a series of agent-based simulations. In general, the results suggest that imprecise fusion operators are optimal when the frequency of fusion is high relative to the frequency with which evidence is obtained from the environment.