Can I read Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation on EtoBox?

Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation by Fu, Bo; Smith, William; Rizzo, Denise; Castanier, Matthew; Ghaffari, Maani; Barton, Kira is a scholarly article available to read on EtoBox.

What is Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation about?

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%.

Author

Fu, Bo; Smith, William; Rizzo, Denise; Castanier, Matthew; Ghaffari, Maani; Barton, Kira

Published

2022

Language

EN