Ask and You Shall be Served: Representing and Solving Multi-agent Optimization Problems with Service Requesters and Providers

TL;DR

This paper introduces SOMAOP, a new framework for distributed multi-agent optimization in service-oriented settings, demonstrating its effectiveness with auction and matching algorithms in emergency rescue scenarios.

Contribution

The paper proposes SOMAOP, a novel framework that addresses limitations of DCOP in service-oriented multi-agent problems, with new algorithms based on auctions and matching.

Findings

Repeated auctions rapidly converge to high-quality solutions.

Matching algorithms converge slower but yield better solutions.

SOMAOP outperforms standard DCOP and greedy algorithms.

Abstract

In scenarios with numerous emergencies that arise and require the assistance of various rescue units (e.g., medical, fire, \& police forces), the rescue units would ideally be allocated quickly and distributedly while aiming to minimize casualties. This is one of many examples of distributed settings with service providers (the rescue units) and service requesters (the emergencies) which we term \textit{service oriented settings}. Allocating the service providers in a distributed manner while aiming for a global optimum is hard to model, let alone achieve, using the existing Distributed Constraint Optimization Problem (DCOP) framework. Hence, the need for a novel approach and corresponding algorithms. We present the Service Oriented Multi-Agent Optimization Problem (SOMAOP), a new framework that overcomes the shortcomings of DCOP in service oriented settings. We evaluate the framework using various algorithms based on auctions and matching algorithms (e.g., Gale Shapely). We empirically show that algorithms based on repeated auctions converge to a high quality solution very fast, while repeated matching problems converge slower, but produce higher quality solutions. We demonstrate the advantages of our approach over standard incomplete DCOP algorithms and a greedy centralized algorithm.