On the Minimal Set of Inputs Required for Efficient Neuro-Evolved Foraging

TL;DR

This study identifies the minimal set of input signals necessary for efficient neuro-evolved foraging, highlighting the importance of resource-switching cues and pheromones in optimizing search and transport behaviors.

Contribution

The paper presents an ablation analysis of eatfa, revealing the most critical input features for effective foraging across various resource distributions.

Findings

Switching signals are essential for resource search and transport.

Pheromones significantly enhance foraging performance.

A minimal input set achieves near-optimal resource collection.

Abstract

In this paper, we perform an ablation study of \neatfa, a neuro-evolved foraging algorithm that has recently been shown to forage efficiently under different resource distributions. Through selective disabling of input signals, we identify a \emph{sufficiently} minimal set of input features that contribute the most towards determining search trajectories which favor high resource collection rates. Our experiments reveal that, independent of how the resources are distributed in the arena, the signals involved in imparting the controller the ability to switch from searching of resources to transporting them back to the nest are the most critical. Additionally, we find that pheromones play a key role in boosting performance of the controller by providing signals for informed locomotion in search for unforaged resources.