A Tractable Truthful Profit Maximization Mechanism Design with Autonomous Agents

TL;DR

This paper introduces a practical, truthful profit maximization mechanism for task allocation with autonomous agents, transforming a complex non-convex problem into a tractable convex optimal control problem and providing a numerical solution approach.

Contribution

It presents a novel incentive mechanism allowing autonomous agents to decide their participation levels, improving upon classical contract theory methods for task allocation.

Findings

Mechanism ensures truthful agent reporting and participation.

Reformulation into a convex optimal control problem for tractability.

Numerical algorithm effectively solves the proposed mechanism.

Abstract

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 paper, 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 truthful mechanism is initially modeled as a non-convex 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.