Comparison of Ant-Inspired Gatherer Allocation Approaches using Memristor-Based Environmental Models

TL;DR

This paper compares three ant-inspired gatherer allocation strategies using memristor-based environmental models, demonstrating their efficiency and adaptability in resource collection tasks.

Contribution

It introduces memristor-based models for evaluating and implementing ant-inspired gatherer allocation strategies in known environments.

Findings

All Site model improves efficiency for similar resource quality.

Leaf Cutter method excels in diverse environments and short-term resource influx.

Memristor abstractions enable effective comparison and control of agent behaviors.

Abstract

Memristors are used to compare three gathering techniques in an already-mapped environment where resource locations are known. The All Site model, which apportions gatherers based on the modeled memristance of that path, proves to be good at increasing overall efficiency and decreasing time to fully deplete an environment, however it only works well when the resources are of similar quality. The Leaf Cutter method, based on Leaf Cutter Ant behaviour, assigns all gatherers first to the best resource, and once depleted, uses the All Site model to spread them out amongst the rest. The Leaf Cutter model is better at increasing resource influx in the short-term and vastly out-performs the All Site model in a more varied environments. It is demonstrated that memristor based abstractions of gatherer models provide potential methods for both the comparison and implementation of agent controls.