# Decentralized Decision-Making Over Multi-Task Networks

## TL;DR

This paper introduces a distributed algorithm enabling agents in multi-task networks to collaboratively select a common objective, improving network performance through localized interactions in both static and mobile settings.

## Contribution

It proposes a novel decentralized decision-making method for multi-task networks with agents observing diverse data models.

## Key findings

- Agents successfully reach agreement on objectives through local interactions.
- The approach enhances network performance in static and mobile scenarios.
- Simulations validate the effectiveness of the proposed strategies.

## Abstract

In important applications involving multi-task networks with multiple objectives, agents in the network need to decide between these multiple objectives and reach an agreement about which single objective to follow for the network. In this work we propose a distributed decision-making algorithm. The agents are assumed to observe data that may be generated by different models. Through localized interactions, the agents reach agreement about which model to track and interact with each other in order to enhance the network performance. We investigate the approach for both static and mobile networks. The simulations illustrate the performance of the proposed strategies.