A novel method for extraction of front boundary agents in multi-agent systems

Area of Science:

• Artificial Intelligence
• Complex Systems
• Topology

Background:

• Distributed multi-agent systems require effective decision-making mechanisms.
• Collective behaviors in biological swarms inspire models for agent coordination.
• Topological concepts like boundary sets offer frameworks for defining agent roles.

Purpose of the Study:

• To introduce and formally define the concept of front boundary agents.
• To develop algorithms for identifying front boundary agents amidst uncertain environmental conditions.
• To evaluate and compare the performance of proposed algorithms against human judgment.

Main Methods:

• Formal definition of the front boundary agent model.
• Development of two algorithms: Upper Convex Hull (UCH) and Fast Front Boundary Detection (FFBD).
• Simulation experiments on regular and random clusters, followed by a questionnaire survey with human participants.

Main Results:

• The Fast Front Boundary Detection (FFBD) algorithm aligns with the 'majority rule' principle.
• FFBD demonstrates superior accuracy and stability compared to the UCH algorithm.
• Evaluation metrics (goodness of fit, Hausdorff distance, mean derivation) confirm FFBD's effectiveness.

Conclusions:

• The FFBD algorithm is a robust method for identifying front boundary agents in distributed systems.
• The study validates algorithmic approaches against human perception in collective behavior analysis.
• Findings contribute to enhanced decision-making capabilities in artificial multi-agent systems.