A Tractable Truthful Profit Maximization Mechanism Design With Autonomous Agents

• 成果类型: Article
• 署名作者: Montazeri, Mina; Kebriaei, Hamed; Araabi, Babak N.
• 署名单位: University of Tehran; Swiss Federal Institutes of Technology Domain; Swiss Federal Laboratories for Materials Science & Technology (EMPA); Institute for Research in Fundamental Sciences IPM
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3360335
• 发表日期: 2024
• 页码: 5439-5445
• 关键词: Task analysis; resource management; Autonomous agents; games; optimization; Integrated circuits; COSTS; Functional optimization; incomplete information; profit maximization mechanism; task allocation

摘要

Task allocation is a crucial process in modern systems, but it is often challenged by incomplete information about the utilities of participating agents. In this article, we propose a new profit maximization mechanism for the task allocation problem, where the task publisher seeks an optimal incentive function to maximize its own profit and simultaneously ensure the truthful announcing of the agent's private information (type) and its participation in the task, while an autonomous agent aims at maximizing its own utility function by deciding on its participation level and announced type. Our mechanism stands out from the classical contract theory-based truthful mechanisms as it empowers agents to make their own decisions about their level of involvement, making it more practical for many real-world task allocation scenarios. It has been proven that by considering a linear form of incentive function consisting of two decision functions for the task publisher the mechanism's goals are met. The proposed mechanism is initially modeled as a nonconvex functional optimization with the double continuum of constraints, nevertheless, we demonstrate that by deriving an equivalent form of the incentive constraints, it can be reformulated as a tractable convex optimal control problem. Further, we propose a numerical algorithm to obtain the solution.