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Josiah-P.-HannaElliot-Fosong
2021
Josiah P. Hanna, Arrasy Rahman, Elliot Fosong, Francisco Eiras, Mihai Dobre, John Redford, Subramanian Ramamoorthy, Stefano V. Albrecht
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
Abstract | BibTex | arXiv
IROSautonomous-drivinggoal-recognitionexplainable-ai
Abstract:
Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When there are unseen obstacles or occluded vehicles in a scenario, goal recognition may be confounded by the effects of these unseen entities on the behaviour of observed vehicles. Existing prediction algorithms that assume rational behaviour with respect to inferred goals may fail to make accurate long-horizon predictions because they ignore the possibility that the behaviour is influenced by such unseen entities. We introduce the Goal and Occluded Factor Inference (GOFI) algorithm which bases inference on inverse-planning to jointly infer a probabilistic belief over goals and potential occluded factors. We then show how these beliefs can be integrated into Monte Carlo Tree Search (MCTS). We demonstrate that jointly inferring goals and occluded factors leads to more accurate beliefs with respect to the true world state and allows an agent to safely navigate several scenarios where other baselines take unsafe actions leading to collisions.
@inproceedings{hanna2021interpretable,
title={Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles},
author={Josiah P. Hanna and Arrasy Rahman and Elliot Fosong and Francisco Eiras and Mihai Dobre and John Redford and Subramanian Ramamoorthy and Stefano V. Albrecht},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2021}
}
Ibrahim H. Ahmed, Josiah P. Hanna, Elliot Fosong, Stefano V. Albrecht
Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction
International Conference on Practical Applications of Agents and Multi-Agent Systems, 2021
Abstract | BibTex | arXiv | Publisher | Code
PAAMSsecurityagent-modelling
Abstract:
Current methods for authentication and key agreement based on public-key cryptography are vulnerable to quantum computing. We propose a novel approach based on artificial intelligence research in which communicating parties are viewed as autonomous agents which interact repeatedly using their private decision models. Authentication and key agreement are decided based on the agents' observed behaviors during the interaction. The security of this approach rests upon the difficulty of modeling the decisions of interacting agents from limited observations, a problem which we conjecture is also hard for quantum computing. We release PyAMI, a prototype authentication and key agreement system based on the proposed method. We empirically validate our method for authenticating legitimate users while detecting different types of adversarial attacks. Finally, we show how reinforcement learning techniques can be used to train server models which effectively probe a client's decisions to achieve more sample-efficient authentication.
@inproceedings{ahmed2021quantum,
title={Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction},
author={Ibrahim H. Ahmed and Josiah P. Hanna and Elliot Fosong and Stefano V. Albrecht},
booktitle={International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS)},
year={2021}
}