Publications

In evidence

(For a full list see below)

Explainable Inference on Sequential Data via Memory-Tracking

We propose an approach to build explanations for sequential tasks exploiting the memory reading and writing operations of Memory Augumented Neural Networks.

Biagio La Rosa, Roberto Capobianco and Daniele Nardi

Paper   Code   BibTeX   Blog Post

@inproceedings{LaRosa20,
title = {Explainable Inference on Sequential Data via Memory-Tracking},
author = {La Rosa, Biagio and Capobianco, Roberto and Nardi, Daniele},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {2006--2013},
year = {2020},
month = {7},
doi = {10.24963/ijcai.2020/278},
url = {https://doi.org/10.24963/ijcai.2020/278},
}

 

Full List

2021
Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies.
Roberto Capobianco, Varun Kompella, James Ault, Guni Sharon, Stacy Jong, Spencer Fox, Lauren Meyers, Peter R. Wurman and Peter Stone.
Journal of Artificial Intelligence Research (JAIR).
Paper   BibTeX  
        @inproceedings{capobianco2021covid,
title={Agent-Based {M}arkov Modeling for Improved {COVID}-19 Mitigation Policies},
author={Roberto Capobianco and Varun Kompella and James Ault and Guni Sharon and Stacy Jong and Spencer Fox and Lauren Meyers and Peter R. Wurman and Peter Stone},
month = {August},
year={2021},
pages = {953--992},
Journal = {Journal of Artificial Intelligence Research (JAIR)},
Volume = {71},
Publisher = {Association for the Advancement of Artificial Intelligence}
}
Learning Transferable Policies for Autonomous Planetary Landing via Deep Reinforcement Learning.
Giulia Ciabatti, Roberto Capobianco, Shreyansh Daftry.
Ascend, AIAA.
Autonomous Planetary Landing via Deep Reinforcement Learning and Transfer Learning.
Giulia Ciabatti, Shreyansh Daftry and Roberto Capobianco.
AI4Space Workshop (CVPR 2021).
Paper   BibTeX  
        @InProceedings{Ciabatti_2021_CVPR,
author = {Ciabatti, Giulia and Daftry, Shreyansh and Capobianco, Roberto},
title = {Autonomous Planetary Landing via Deep Reinforcement Learning and Transfer Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021},
pages = {2031-2038} }
Multiagent Epidemiologic Inference through Realtime Contact Tracing.
Guni Sharon, James Ault, Peter Stone, Varun Kompella and Roberto Capobianco.
Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021).
Paper   BibTeX  
        @inproceedings{sharon2021covid,
title={Multiagent Epidemiologic Inference through Realtime Contact Tracing},
author={Sharon, Guni and Ault, James and Stone, Peter and Kompella, Varun and Capobianco, Roberto},
booktitle={Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2021)},
location = {London, UK},
month = {May},
year={2021},
organization={International Foundation for Autonomous Agents and Multiagent Systems}
}
2020
LoOP: Iterative learning for optimistic planning on robots.
Francesco Riccio, Roberto Capobianco and Daniele Nardi.
Robotics and Autonomous Systems.
Paper   BibTeX  
        @article{RICCIO2021103693,
title = {LoOP: Iterative learning for optimistic planning on robots},
journal = {Robotics and Autonomous Systems},
abstract = {Efficient robotic behaviors require robustness and adaptation to dynamic changes of the environment, whose characteristics rapidly vary during robot operation. To generate effective robot action policies, planning and learning techniques have shown the most promising results. However, if considered individually, they present different limitations. Planning techniques lack generalization among similar states and require experts to define behavioral routines at different levels of abstraction. Conversely, learning methods usually require a considerable number of training samples and iterations of the algorithm. To overcome these issues, and to efficiently generate robot behaviors, we introduce LoOP, an iterative learning algorithm for optimistic planning that combines state-of-the-art planning and learning techniques to generate action policies. The main contribution of LoOP is the combination of Monte-Carlo Search Planning and Q-learning, which enables focused exploration during policy refinement in different robotic applications. We demonstrate the robustness and flexibility of LoOP in various domains and multiple robotic platforms, by validating the proposed approach with an extensive experimental evaluation.}
volume = {136},
pages = {103693},
year = {2021},
issn = {0921-8890},
doi = {https://doi.org/10.1016/j.robot.2020.103693},
url = {http://www.sciencedirect.com/science/article/pii/S0921889020305339},
author = {Francesco Riccio and Roberto Capobianco and Daniele Nardi},
keywords = {Autonomous planning and learning, Monte-Carlo planning, Q-learning, Deep robot reinforcement learning},
}
Reinforcement Learning for Optimization of COVID-19 Mitigation policies.
Varun Kompella, Roberto Capobianco, Stacy Jong, Jonathan Browne, Spencer Fox, Lauren Meyers, Peter Wurman and Peter Stone.
arXiv preprint.
Paper   Code   BibTeX  
        @misc{kompella2020reinforcement,
title={Reinforcement Learning for Optimization of COVID-19 Mitigation policies},
author={Varun Kompella and Roberto Capobianco and Stacy Jong and Jonathan Browne and Spencer Fox and Lauren Meyers and Peter Wurman and Peter Stone},
year={2020},
eprint={2010.10560},
archivePrefix={arXiv},
primaryClass={cs.LG} }
GUESs: Generative modeling of Unknown Environments and Spatial Abstraction for Robots.
Francesco Riccio, Roberto Capobianco and Daniele Nardi.
Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems.
Paper   BibTeX  
        @inproceedings{10.5555/3398761.3399047,
author = {Riccio, Francesco and Capobianco, Roberto and Nardi, Daniele},
title = {GUESs: Generative Modeling of Unknown Environments and Spatial Abstraction for Robots},
year = {2020},
isbn = {9781450375184},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
abstract = {Representing unknown and missing knowledge about the environment is fundamental to leverage robot behavior and improve its performance in completing a task. However, reconstructing spatial knowledge beyond the sensory horizon of the robot is an extremely challenging task. Existing approaches assume that the environment static and features repetitive patterns (e.g. rectangular rooms) or that it can be all generalized with pre-trained models. Our goal is to remove such assumptions and to introduce a novel methodology that allows the robot to represent unknown spatial knowledge in dynamic and unstructured environments. To this end, we exploit generative learning to (1) learn a distribution of spatial landmarks observed during the robot mission and to (2) generate missing information in real-time. The proposed approach aims at supporting planning and decision-making processes needed for robot behaviors. In this paper, we describe architecture modeling the proposed approach and a first validation on a mobile platform.}, booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
pages = {1978–1980},
numpages = {3},
keywords = {generative learning, knowledge representation, robot learning},
location = {Auckland, New Zealand},
series = {AAMAS '20} }
Explainable Inference on Sequential Data via Memory-Tracking.
Biagio La Rosa, Roberto Capobianco and Daniele Nardi.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20).
Paper   Code   BibTeX   Blog Post
        @inproceedings{LaRosa20,
title = {Explainable Inference on Sequential Data via Memory-Tracking},
author = {La Rosa, Biagio and Capobianco, Roberto and Nardi, Daniele},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {2006--2013},
year = {2020},
month = {7},
doi = {10.24963/ijcai.2020/278},
url = {https://doi.org/10.24963/ijcai.2020/278},
}