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

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