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.

翻译：在众多突发事件需要各类救援单位（如医疗、消防和警察部队）协助的场景中，理想情况下应快速且分布式地调配救援单位，同时以最小化伤亡为目标。这是服务提供者（救援单位）与服务请求者（突发事件）组成的分布式环境的一个典型例子，我们将其称为服务导向型场景。在现有分布式约束优化问题（DCOP）框架下，以全局最优为目标对服务提供者进行分布式分配难以建模，更遑论实现。因此，亟需提出新颖的方法和相应的算法。本文提出面向服务的多智能体优化问题（SOMAOP），这是一个克服了DCOP在服务导向场景中缺陷的新框架。我们利用基于拍卖和匹配算法（如盖尔-沙普利算法）的各种算法对该框架进行了评估。实验表明，基于重复拍卖的算法能快速收敛到高质量解，而重复匹配算法虽收敛较慢，但能产生更优的解。我们展示了该方法相较于标准非精确DCOP算法和贪婪集中式算法的优势。