Grounded QA

Summary: Documentation and files about the Grounded QA component, as part of the GUT-AI Initiative.

Table of Contents

• About
• Main papers
  • Selected publications
  • References
• Component files
  • Component page
  • Component DOI
  • Component dependencies
  • Environment simulators
  • Datasets
  • Model Zoos
• Getting involved
• How to cite this
• License

About

The purpose of this component is to perform Grounded Question Answering (Grounded QA) by applying Grounded Cognition on QA tasks on multiple mobile robots or multiple aerial robots (drones) or a combination of them using Multimodal Learning (i.e. visuo-linguistic abilities)

Main papers

Selected publications

• Kourouklides, I. (2022). Bayesian Deep Multi-Agent Multimodal Reinforcement Learning for Embedded Systems in Games, Natural Language Processing and Robotics. OSF Preprints. https://doi.org/10.31219/osf.io/sjrkh

References

See References.

Component files

Component page

Thanks to OSF (by the Center for Open Science), the project is temporarily hosted at:

• https://osf.io/8frxm/

Component DOI

Project identifier: https://doi.org/10.17605/OSF.IO/8FRXM

Component dependencies

This component depends on the following components of GUT-AI:

• QA
• VSPT
• Multi-Agent Communication
• Multi-Robot Path Planning

Environment simulators

See Simulators.

Datasets

See Datasets.

Model Zoos

See Model Zoos.

Getting involved

• Community Discord for collaboration and discussion.

How to cite this

If you want to do so, feel free to cite this component in your publications:

@article{kourouklides2022gqa,
  author = {Ioannis Kourouklides},
  journal = {OSF Preprints},
  title = {Grounded QA},
  year = {2022},
  doi = {10.17605/osf.io/8frxm},
  license = {Creative Commons Zero CC0 1.0}
}

License

Creative Commons Zero CC0 1.0 (Public Domain)