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 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 to overcome DCOP's shortcomings in service oriented settings. We evaluate the framework using algorithms based on auctions and matching (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.
Original language | English |
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Pages (from-to) | 77-85 |
Number of pages | 9 |
Journal | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
Volume | 2023-May |
State | Published - 1 Jan 2023 |
Event | 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 |
Keywords
- Distributed Constraint Optimization Problems
- Distributed Problem Solving
- Multi-Agent Optimization
- Multi-Agent System
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering