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Task allocation is a crucial process in modern systems, but it is oftenchallenged by incomplete information about the utilities of participatingagents. In this paper, we propose a new profit maximization mechanism for thetask allocation problem, where the task publisher seeks an optimal incentivefunction to maximize its own profit and simultaneously ensure the truthfulannouncing of the agent's private information (type) and its participation inthe task, while an autonomous agent aims at maximizing its own utility functionby deciding on its participation level and announced type. Our mechanism standsout from the classical contract theory-based truthful mechanisms as it empowersagents to make their own decisions about their level of involvement, making itmore practical for many real-world task allocation scenarios. It has beenproven that by considering a linear form of incentive function consisting oftwo decision functions for the task publisher the mechanism's goals are met.The proposed truthful mechanism is initially modeled as a non-convex functionaloptimization with the double continuum of constraints, nevertheless, wedemonstrate 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.

A Tractable Truthful Profit Maximization Mechanism Design with Autonomous Agents