# Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

arXiv: https://arxiv.org/abs/1711.06871 · HTML: https://mlsanity.com/paper/1711.06871/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation](https://mlsanity.com/paper/2411.19526/) (2024)
- [The Viability of Domain Constrained Coalition Formation for Robotic Collectives](https://mlsanity.com/paper/2306.05590/) (2023)
- [GRAPE-S: Near Real-Time Coalition Formation for Multiple Service Collectives](https://mlsanity.com/paper/2310.12480/) (2023)
- [Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach](https://mlsanity.com/paper/2401.00661/) (2024)
- [SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms](https://mlsanity.com/paper/2409.04230/) (2024)

## Abstract (arXiv)

This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a strongly-connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the solution, and additionally show that 50% of suboptimality can be at least guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation.