# Distributed Algorithm for Matching between Individuals and Activities

## TL;DR

This paper presents a distributed, agent-based algorithm for matching individuals to activities, improving matching quality and speed compared to classical methods.

## Contribution

It introduces two novel clearing-house mechanisms for coalition formation that optimize satisfaction and preferences, outperforming traditional search techniques.

## Key findings

- Algorithms produce better matchings than classical methods.
- Distributed approach speeds up runtime.
- Mechanisms effectively balance satisfaction and preferences.

## Abstract

In this paper, we introduce an agent-based model for coalition formation which is suitable for our usecase. We propose here two clearing-houses mechanisms that return sound matchings. The first aims at maximizing the global satisfaction of the individuals. The second ensures that all individuals are assigned as much as possible to a preferred activity. Our experiments show that the outcome of our algorithms are better than those obtained with the classical search/optimization techniques. Moreover, their distribution speeds up their runtime.