In the realm of multi-agent systems, diversity plays an instrumental role. Harnessing multi-agent teams' maximum efficiency requires a keen understanding of multiple factors that sway a system either way on the diversity scale. A metric for diversity is discussed here and a novel concept of transient diversity is introduced. Finally, an experiment focusing on social entropy involving a RoboCup soccer team is presented.

This research area may be nascent, but we are confident that improved diversity management for multi-agent systems can directly benefit existing agent-based applications and drive development in future multi-robot technologies.

1 INTRODUCTION

The quest for teamwork efficiency often leads us to dynamic coordination between team members or intelligent task and skill delegation. However, our belief is that a certain extent of agent specialization, resulting in bounded but significant heterogeneity, is key for achieving effective task completion. At the same time, it's crucial to ensure coherency within the team. Hence, we propose a model that assimilates various factors that impact a multi-agent system's overall diversity - factors that we term as diversity drivers.

1.1 BACKGROUND

1.1.1 AGENTS

Agents, an evolution of programming objects, react based on current situations while considering individual and group objectives. They are essential in multiple systems and can greatly reduce complexity, thus contributing to the development of advanced robotics and intuitive computer systems. This in turn, allows a seamless integration of information technology with human society.

1.1.2 TEAMWORK

Efficient teamwork is foundational for high-performance in many domains, including robotics. The focus is on managing the complexity of interactions between agents and developing effective methods to model agent societies. Researchers are exploring social exchange using game theory to enhance the effectiveness of teamwork.

2 HETEROGENEITY DRIVERS

In our model, we consider technical differences, role assignments, individual traits and skills, and global evaluation of performance as heterogeneity drivers. These factors, collectively referred to as the diversifying force, oscillate towards either end of the diversity scale over time.

3 HOMOGENEITY DRIVERS

Homogeneity drivers, on the other hand, include engineering constraints, standards and agreements, behavior imitation, and local evaluation of performance. These factors incline the system towards uniformity, thus reducing diversity.

4 DIVERSITY

Diversity is further categorized into behavioral differences, social entropy, behavioral diversity, and case-based diversity. A quantitative metric for diversity is crucial, as it allows the correlation of diversity with performance.

5 TRANSIENT DIVERSITY

The concept of transient diversity explains how a diversity mechanism designed for efficient teamwork can potentially encompass a risk of counterproductive behavioral oscillations. This is typified by diversity change and stabilization, diversity vibration, and the analogies to other systems.

6 EXPERIMENT

An experimental implementation of these ideas on a RoboCup simulated soccer team is presented here. This brings the theoretical model to life and lends empirical evidence to the study.

It is clear that diversity, including its management and metrics, is crucial in multi-agent systems. The concept of transient diversity provides a fresh angle to the longstanding discussion on cooperation and competition in a team, thus opening a new frontier for exploration.