Adversarial Task Assignment

TL;DR

This paper studies the problem of assigning tasks to workers in the presence of adversarial attacks, providing optimal and approximate algorithms for homogeneous and heterogeneous tasks, supported by theoretical analysis and experiments.

Contribution

It introduces the first algorithms for adversarial task assignment, including an optimal solution for homogeneous tasks and an approximation for heterogeneous tasks, addressing a previously underexplored problem.

Findings

Efficient algorithm for optimal assignment with homogeneous tasks.

NP-Hardness of the problem with heterogeneous tasks.

Experimental validation of algorithm effectiveness.

Abstract

The problem of assigning tasks to workers is of long-standing fundamental importance. Examples of this include the classical problem of assigning computing tasks to nodes in a distributed computing environment, assigning jobs to robots, and crowdsourcing. Extensive research into this problem generally addresses important issues such as uncertainty and incentives. However, the problem of adversarial tampering with the task assignment process has not received as much attention. We are concerned with a particular adversarial setting where an attacker may target a set of workers in order to prevent the tasks assigned to these workers from being completed. When all tasks are homogeneous, we provide an efficient algorithm for computing the optimal assignment. When tasks are heterogeneous, we show that the adversarial assignment problem is NP-Hard, and present an algorithm for solving it approximately. Our theoretical results are accompanied by extensive experiments showing the effectiveness of our algorithms.