Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation
Abstract
This paper presents a learning framework to estimate an agent capability and task requirement model for multi-agent task allocation. With a set of team configurations and the corresponding task performances as the training data, linear task constraints can be learned to be embedded in many existing optimization-based task allocation frameworks. Comprehensive computational evaluations are conducted to test the scalability and prediction accuracy of the learning framework with a limited number of team configurations and performance pairs. A ROS and Gazebo-based simulation environment is developed to validate the proposed requirements learning and task allocation framework in practical multi-agent exploration and manipulation tasks. Results show that the learning process for scenarios with 40 tasks and 6 types of agents uses around 12 seconds, ending up with prediction errors in the range of 0.5-2%.
Cite
@article{arxiv.2211.03286,
title = {Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation},
author = {Bo Fu and William Smith and Denise Rizzo and Matthew Castanier and Maani Ghaffari and Kira Barton},
journal= {arXiv preprint arXiv:2211.03286},
year = {2022}
}
Comments
The video and open-source code are at https://brg.engin.umich.edu/publications/learn-multiagent-taskreq/