# Decentralized Game-Theoretic Control for Dynamic Task Allocation Problems for Multi-Agent Systems

arXiv: https://arxiv.org/abs/2009.08628 · HTML: https://mlsanity.com/paper/2009.08628/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents](https://mlsanity.com/paper/2303.16641/) (2023)
- [Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey](https://mlsanity.com/paper/2306.09445/) (2023)

## Abstract (arXiv)

We propose a decentralized game-theoretic framework for dynamic task allocation problems for multi-agent systems. In our problem formulation, the agents' utilities depend on both the rewards and the costs associated with the successful completion of the tasks assigned to them. The rewards reflect how likely is for the agents to accomplish their assigned tasks whereas the costs reflect the effort needed to complete these tasks (this effort is determined by the solution of corresponding optimal control problems). The task allocation problem considered herein corresponds to a dynamic game whose solution depends on the states of the agents in contrast with classic static (or single-act) game formulations. We propose a greedy solution approach in which the agents negotiate with each other to find a mutually agreeable (or individually rational) task assignment profile based on evaluations of the task utilities that reflect their current states. We illustrate the main ideas of this work by means of extensive numerical simulations.