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We consider an outsourcing problem where a software agent procures multipleservices from providers with uncertain reliabilities to complete acomputational task before a strict deadline. The service consumer requires aprocurement strategy that achieves the optimal balance between successprobability and invocation cost. However, the service providers areself-interested and may misrepresent their private cost information if itbenefits them. For such settings, we design a novel procurement auction thatprovides the consumer with the highest possible revenue, while givingsufficient incentives to providers to tell the truth about their costs. Thisauction creates a contingent plan for gradual service procurement that suggestsrecruiting a new provider only when the success probability of the alreadyhired providers drops below a time-dependent threshold. To make this auctionincentive compatible, we propose a novel weighted threshold payment schemewhich pays the minimum among all truthful mechanisms. Using the weightedpayment scheme, we also design a low-complexity near-optimal auction thatreduces the computational complexity of the optimal mechanism by 99% with onlymarginal performance loss (less than 1%). We demonstrate the effectiveness andstrength of our proposed auctions through both game theoretical and numericalanalysis. The experiment results confirm that the proposed auctions exhibit 59%improvement in performance over the current state-of-the-art, by increasingsuccess probability up to 79% and reducing invocation cost by up to 11%.

Optimal Auction Design for the Gradual Procurement of Strategic Service Provider Agents