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surveydeep-rlmulti-agent-rlagent-modellingad-hoc-teamworkautonomous-drivinggoal-recognitionexplainable-aicausalgeneralisationsecurityemergent-communicationiterated-learningintrinsic-rewardsimulatorstate-estimationdeep-learningtransfer-learning
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Cheng-Wang
2024
Anton Kuznietsov, Balint Gyevnar, Cheng Wang, Steven Peters, Stefano V. Albrecht
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
IEEE Transactions on Intelligent Transportation Systems, 2024
Abstract | BibTex | arXiv
T-ITSautonomous-drivingexplainable-aisurvey
Abstract:
Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, inscrutable AI systems exacerbate the existing challenge of safety assurance of AD. One way to mitigate this challenge is to utilize explainable AI (XAI) techniques. To this end, we present the first comprehensive systematic literature review of explainable methods for safe and trustworthy AD. We begin by analyzing the requirements for AI in the context of AD, focusing on three key aspects: data, model, and agency. We find that XAI is fundamental to meeting these requirements. Based on this, we explain the sources of explanations in AI and describe a taxonomy of XAI. We then identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation. Finally, we propose a modular framework called SafeX to integrate these contributions, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.
@article{kuznietsov2024avreview,
title={Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review},
author={Anton Kuznietsov and Balint Gyevnar and Cheng Wang and Steven Peters and Stefano V. Albrecht},
journal={IEEE Transactions on Intelligent Transportation Systems (T-ITS)},
year={2024}
}
Balint Gyevnar, Cheng Wang, Christopher G. Lucas, Shay B. Cohen, Stefano V. Albrecht
Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
International Conference on Autonomous Agents and Multi-Agent Systems, 2024
Abstract | BibTex | arXiv | Code | Dataset
AAMASexplainable-aiautonomous-drivingcausal
Abstract:
We present CEMA: Causal Explanations in Multi-Agent systems; a framework for creating causal natural language explanations of an agent's decisions in dynamic sequential multi-agent systems to build more trustworthy autonomous agents. Unlike prior work that assumes a fixed causal structure, CEMA only requires a probabilistic model for forward-simulating the state of the system. Using such a model, CEMA simulates counterfactual worlds that identify the salient causes behind the agent's decisions. We evaluate CEMA on the task of motion planning for autonomous driving and test it in diverse simulated scenarios. We show that CEMA correctly and robustly identifies the causes behind the agent's decisions, even when a large number of other agents is present, and show via a user study that CEMA's explanations have a positive effect on participants' trust in autonomous vehicles and are rated as high as high-quality baseline explanations elicited from other participants.
@inproceedings{gyevnar2024cema,
title={Causal Explanations for Sequential Decision-Making in Multi-Agent Systems},
author={Balint Gyevnar and Cheng Wang and Christopher G. Lucas and Shay B. Cohen and Stefano V. Albrecht},
booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems},
year={2024}
}
2023
Cillian Brewitt, Massimiliano Tamborski, Cheng Wang, Stefano V. Albrecht
Verifiable Goal Recognition for Autonomous Driving with Occlusions
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023
Abstract | BibTex | arXiv
IROSautonomous-drivinggoal-recognitionexplainable-ai
Abstract:
Goal recognition (GR) allows the future behaviour of vehicles to be more accurately predicted. GR involves inferring the goals of other vehicles, such as a certain junction exit. In autonomous driving, vehicles can encounter many different scenarios and the environment is partially observable due to occlusions. We present a novel GR method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT). We demonstrate that OGRIT can handle missing data due to occlusions and make inferences across multiple scenarios using the same learned decision trees, while still being fast, accurate, interpretable and verifiable. We also present the inDO and rounDO datasets of occluded regions used to evaluate OGRIT.
@inproceedings{brewitt2023ogrit,
title={Verifiable Goal Recognition for Autonomous Driving with Occlusions},
author={Cillian Brewitt and Massimiliano Tamborski and Cheng Wang and Stefano V. Albrecht},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},
year={2023}
}
Cillian Brewitt, Massimiliano Tamborski, Cheng Wang, Stefano V. Albrecht
Verifiable Goal Recognition for Autonomous Driving with Occlusions
ICRA Workshop on Scalable Autonomous Driving, 2023
Abstract | BibTex | arXiv
ICRAautonomous-drivinggoal-recognitionexplainable-ai
Abstract:
Goal recognition (GR) allows the future behaviour of vehicles to be more accurately predicted. GR involves inferring the goals of other vehicles, such as a certain junction exit. In autonomous driving, vehicles can encounter many different scenarios and the environment is partially observable due to occlusions. We present a novel GR method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT). We demonstrate that OGRIT can handle missing data due to occlusions and make inferences across multiple scenarios using the same learned decision trees, while still being fast, accurate, interpretable and verifiable. We also present the inDO and rounDO datasets of occluded regions used to evaluate OGRIT.
@misc{brewitt2023verifiable,
title={Verifiable Goal Recognition for Autonomous Driving with Occlusions},
author={Cillian Brewitt and Massimiliano Tamborski and Cheng Wang and Stefano V. Albrecht},
booktitle={ICRA 2023 Workshop on Scalable Autonomous Driving},
year={2023}
}
Balint Gyevnar, Cheng Wang, Christopher G. Lucas, Shay B. Cohen, Stefano V. Albrecht
Causal Social Explanations for Stochastic Sequential Multi-Agent Decision-Making
AAMAS Workshop on Explainable and Transparent AI and Multi-Agent Systems, 2023
Abstract | BibTex | arXiv | Code
AAMASautonomous-drivingexplainable-aicausal
Abstract:
We present a novel framework to generate causal explanations for the decisions of agents in stochastic sequential multi-agent environments. Explanations are given via natural language conversations answering a wide range of user queries and requiring associative, interventionist, or counterfactual causal reasoning. Instead of assuming any specific causal graph, our method relies on a generative model of interactions to simulate counterfactual worlds which are used to identify the salient causes behind decisions. We implement our method for motion planning for autonomous driving and test it in simulated scenarios with coupled interactions. Our method correctly identifies and ranks the relevant causes and delivers concise explanations to the users' queries.
@inproceedings{gyevnar2023causal,
title={Causal Social Explanations for Stochastic Sequential Multi-Agent Decision-Making},
author={Balint Gyevnar and Cheng Wang and Christopher G. Lucas and Shay B. Cohen and Stefano V. Albrecht},
booktitle={5th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems},
year={2023}
}
2022
Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht
Deep Reinforcement Learning for Multi-Agent Interaction
AI Communications, 2022
Abstract | BibTex | arXiv | Publisher
AICsurveydeep-rlmulti-agent-rlad-hoc-teamworkagent-modellinggoal-recognitionsecurityexplainable-aiautonomous-driving
Abstract:
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep reinforcement learning and multi-agent reinforcement learning. Research problems include scalable learning of coordinated agent policies and inter-agent communication; reasoning about the behaviours, goals, and composition of other agents from limited observations; and sample-efficient learning based on intrinsic motivation, curriculum learning, causal inference, and representation learning. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.
@article{albrecht2022aic,
author = {Ahmed, Ibrahim H. and Brewitt, Cillian and Carlucho, Ignacio and Christianos, Filippos and Dunion, Mhairi and Fosong, Elliot and Garcin, Samuel and Guo, Shangmin and Gyevnar, Balint and McInroe, Trevor and Papoudakis, Georgios and Rahman, Arrasy and Schäfer, Lukas and Tamborski, Massimiliano and Vecchio, Giuseppe and Wang, Cheng and Albrecht, Stefano V.},
title = {Deep Reinforcement Learning for Multi-Agent Interaction},
journal = {AI Communications, Special Issue on Multi-Agent Systems Research in the UK},
year = {2022}
}
Balint Gyevnar, Massimiliano Tamborski, Cheng Wang, Christopher G. Lucas, Shay B. Cohen, Stefano V. Albrecht
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning
IJCAI Workshop on Artificial Intelligence for Autonomous Driving, 2022
Abstract | BibTex | arXiv | Code
IJCAIautonomous-drivingexplainable-aicausal
Abstract:
Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and queryable systems to increase trust levels. We propose a transparent, human-centric explanation generation method for autonomous vehicle motion planning and prediction based on an existing white-box system called IGP2. Our method integrates Bayesian networks with context-free generative rules and can give causal natural language explanations for the high-level driving behaviour of autonomous vehicles. Preliminary testing on simulated scenarios shows that our method captures the causes behind the actions of autonomous vehicles and generates intelligible explanations with varying complexity.
@inproceedings{gyevnar2022humancentric,
title={A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning},
author={Balint Gyevnar and Massimiliano Tamborski and Cheng Wang and Christopher G. Lucas and Shay B. Cohen and Stefano V. Albrecht},
booktitle={IJCAI Workshop on Artificial Intelligence for Autonomous Driving},
year={2022}
}