Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation

TL;DR

This paper introduces a learning framework for estimating agent capabilities and task requirements in multi-agent task allocation, enabling improved optimization and practical validation in simulation environments.

Contribution

It develops a scalable learning method to model agent and task requirements from performance data, integrating it into existing task allocation frameworks.

Findings

Prediction errors are within 0.5-2% for complex scenarios.

The learning process takes around 12 seconds for 40 tasks and 6 agent types.

The framework is validated in ROS and Gazebo simulation environments.

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%.