Publications
2024
A survey of safety and trustworthiness of large language models through the lens of verification and validation
Huang, X., Ruan, W., Huang, W., Jin, G., Dong, Y., Wu, C., . . . Mustafa, M. A. (n.d.). A survey of safety and trustworthiness of large language models through the lens of verification and validation. Artificial Intelligence Review, 57(7). doi:10.1007/s10462-024-10824-0
Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems
Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2024). Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 5(3), 1143-1153. doi:10.1109/jestie.2023.3317803
Towards Fairness-Aware Adversarial Learning
Zhang, Y., Zhang, T., Mu, R., Huang, X., & Ruan, W. (2024). Towards Fairness-Aware Adversarial Learning. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 24746-24755). IEEE. doi:10.1109/cvpr52733.2024.02337
Formal verification of robustness and resilience of learning-enabled state estimation systems
Huang, W., Zhou, Y., Jin, G., Sun, Y., Meng, J., Zhang, F., & Huang, X. (2024). Formal verification of robustness and resilience of learning-enabled state estimation systems. Neurocomputing, 585, 127643. doi:10.1016/j.neucom.2024.127643
Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment
Hu, Y., Dong, B., Huang, K., Ding, L., Wang, W., Huang, X., & Wang, Q. -F. (2024). Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment. ACM Transactions on Multimedia Computing, Communications, and Applications, 20(4), 1-20. doi:10.1145/3633517
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation
Wu, W., Dai, T., Huang, X., Ma, F., & Xiao, J. (2024). Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Vol. 33 (pp. 6175-6179). IEEE. doi:10.1109/icassp48485.2024.10447893
Two-Stage Transfer Learning for Fusion and Classification of Airborne Hyperspectral Imagery
Rise, B., Uney, M., & Huang, X. (2024). Two-Stage Transfer Learning for Fusion and Classification of Airborne Hyperspectral Imagery. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Vol. abs/2105.07921 (pp. 6555-6559). IEEE. doi:10.1109/icassp48485.2024.10445916
Negative Hesitation Fuzzy Sets and Their Application to Pattern Recognition
Yang, Y., Lee, S., Zhang, H., Huang, X., & Pedrycz, W. (2024). Negative Hesitation Fuzzy Sets and Their Application to Pattern Recognition. IEEE Transactions on Fuzzy Systems, 32(4), 1836-1847. doi:10.1109/tfuzz.2023.3336673
MathAttack: Attacking Large Language Models towards Math Solving Ability
Zhou, Z., Wang, Q., Jin, M., Yao, J., Ye, J., Liu, W., . . . Huang, K. (n.d.). MathAttack: Attacking Large Language Models towards Math Solving Ability. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 19750-19758). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i17.29949
Representation-Based Robustness in Goal-Conditioned Reinforcement Learning
Yin, X., Wu, S., Liu, J., Fang, M., Zhao, X., Huang, X., & Ruan, W. (n.d.). Representation-Based Robustness in Goal-Conditioned Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 21761-21769). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i19.30176
Reward Certification for Policy Smoothed Reinforcement Learning
Mu, R., Soriano Marcolino, L., Zhang, Y., Zhang, T., Huang, X., & Ruan, W. (n.d.). Reward Certification for Policy Smoothed Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 21429-21437). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i19.30139
Hierarchical Distribution-aware Testing of Deep Learning
Huang, W., Zhao, X., Banks, A., Cox, V., & Huang, X. (2024). Hierarchical Distribution-aware Testing of Deep Learning. ACM Transactions on Software Engineering and Methodology, 33(2), 1-35. doi:10.1145/3625290
Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems
Dong, Y., Zhao, X., Wang, S., & Huang, X. (2024). Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems. IEEE Robotics and Automation Letters, 1-8. doi:10.1109/lra.2024.3364471
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting
Dong, Y., Wang, Y., Gama, M., Mustafa, M. A., Deconinck, G., & Huang, X. (2024). Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting. IEEE Internet of Things Journal, 1. doi:10.1109/jiot.2024.3362587
Bridging formal methods and machine learning with model checking and global optimisation
Bensalem, S., Huang, X., Ruan, W., Tang, Q., Wu, C., & Zhao, X. (2024). Bridging formal methods and machine learning with model checking and global optimisation. Journal of Logical and Algebraic Methods in Programming, 137, 100941. doi:10.1016/j.jlamp.2023.100941
Analysis on the Hesitation and its Application to Decision Making
Yang, Y., Lee, S., Kim, K. S., Zhang, H., Huang, X., & Pedrycz, W. (2024). Analysis on the Hesitation and its Application to Decision Making. Decision Making: Applications in Management and Engineering, 7(2), 15-34. doi:10.31181/dmame722024978
Nrat: towards adversarial training with inherent label noise
Chen, Z., Wang, F., Mu, R., Xu, P., Huang, X., & Ruan, W. (2024). Nrat: towards adversarial training with inherent label noise. Machine Learning. doi:10.1007/s10994-023-06437-3
Continuous Engineering for Trustworthy Learning-Enabled Autonomous Systems
Bensalem, S., Katsaros, P., Ničković, D., Liao, B. H. -C., Nolasco, R. R., Ahmed, M. A. E. S., . . . Wu, C. (2024). Continuous Engineering for Trustworthy Learning-Enabled Autonomous Systems. In Unknown Conference (pp. 256-278). Springer Nature Switzerland. doi:10.1007/978-3-031-46002-9_15
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks
Liu, J., Yi, X., & Huang, X. (2024). DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks. IEEE Transactions on Artificial Intelligence, 1-14. doi:10.1109/tai.2024.3440223
Position: Building Guardrails for Large Language Models Requires Systematic Design
Dong, Y., Mu, R., Jin, G., Qi, Y., Hu, J., Zhao, X., . . . Huang, X. (2024). Position: Building Guardrails for Large Language Models Requires Systematic Design. In Proceedings of Machine Learning Research Vol. 235 (pp. 11375-11394).
Progressive Supervision for Tampering Localization in Document Images
Shao, H., Huang, K., Wang, W., Huang, X., & Wang, Q. (2024). Progressive Supervision for Tampering Localization in Document Images. In Unknown Conference (pp. 140-151). Springer Nature Singapore. doi:10.1007/978-981-99-8184-7_11
Survey on Acceleration Techniques for Complete Neural Network Verification
Liu, Z. X., Yang, P. F., Zhang, L. J., Wu, Z. L., & Huang, X. W. (2024). Survey on Acceleration Techniques for Complete Neural Network Verification. Ruan Jian Xue Bao/Journal of Software, 35(9). doi:10.13328/j.cnki.jos.007127
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems
Bensalem, S., Cheng, C. -H., Huang, W., Huang, X., Wu, C., & Zhao, X. (2024). What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems. In Unknown Conference (pp. 55-76). Springer Nature Switzerland. doi:10.1007/978-3-031-46002-9_4
2023
A Symbolic Characters Aware Model for Solving Geometry Problems
Ning, M., Wang, Q. -F., Huang, K., & Huang, X. (2023). A Symbolic Characters Aware Model for Solving Geometry Problems. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 7767-7775). ACM. doi:10.1145/3581783.3612570
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Huang, W., Zhao, X., Jin, G., & Huang, X. (2023). SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE. doi:10.1109/iccv51070.2023.00190
STPA for Learning-Enabled Systems: A Survey and A New Practice
Qi, Y., Dong, Y., Khastgir, S., Jennings, P., Zhao, X., & Huang, X. (2023). STPA for Learning-Enabled Systems: A Survey and A New Practice. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) Vol. 15 (pp. 1381-1388). IEEE. doi:10.1109/itsc57777.2023.10422520
Model Checking for Probabilistic Multiagent Systems
Fu, C., Turrini, A., Huang, X., Song, L., Feng, Y., & Zhang, L. -J. (2023). Model Checking for Probabilistic Multiagent Systems. Journal of Computer Science and Technology, 38(5), 1162-1186. doi:10.1007/s11390-022-1218-6
An accident prediction architecture based on spatio-clock stochastic and hybrid model for autonomous driving safety
Wang, J., Huang, Z., Huang, X., Wang, T., Shen, G., & Xie, J. (2023). An accident prediction architecture based on spatio-clock stochastic and hybrid model for autonomous driving safety. In CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE Vol. 35. doi:10.1002/cpe.6550
Robust Bayesian Abstraction of Neural Networks
Alshareef, A., Berthier, N., Schewe, S., & Huang, X. (2023). Robust Bayesian Abstraction of Neural Networks. In 2023 International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 34 (pp. 276-283). IEEE. doi:10.1109/icmlc58545.2023.10327954
Towards Verifying the Geometric Robustness of Large-Scale Neural Networks
Wang, F., Xu, P., Ruan, W., & Huang, X. (n.d.). Towards Verifying the Geometric Robustness of Large-Scale Neural Networks. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 37 (pp. 15197-15205). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v37i12.26773
Sora: Scalable Black-Box Reachability Analyser on Neural Networks
Xu, P., Wang, F., Ruan, W., Zhang, C., & Huang, X. (2023). Sora: Scalable Black-Box Reachability Analyser on Neural Networks. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Vol. 34 (pp. 1-5). IEEE. doi:10.1109/icassp49357.2023.10097180
Generalizing universal adversarial perturbations for deep neural networks
Zhang, Y., Ruan, W., Wang, F., & Huang, X. (2023). Generalizing universal adversarial perturbations for deep neural networks. MACHINE LEARNING, 112(5), 1597-1626. doi:10.1007/s10994-023-06306-z
Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings
Cai, K., Lu, C. X., & Huang, X. (2023). Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings. IEEE Robotics and Automation Letters, 8(5), 2558-2565. doi:10.1109/lra.2023.3256085
Model-Agnostic Reachability Analysis on Deep Neural Networks
Short-term Load Forecasting with Distributed Long Short-Term Memory
Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2023). Short-term Load Forecasting with Distributed Long Short-Term Memory. In 2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT. doi:10.1109/ISGT51731.2023.10066368
Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning
Meng, Y., Bridge, J., Zhao, Y., Joddrell, M., Qiao, Y., Yang, X., . . . Zheng, Y. (2023). Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 24(3), 3422-3437. doi:10.1109/TITS.2022.3226504
The AAAI-2023 Workshop on Artificial Intelligence Safety (SafeAI-2023)
Pedroza, G., Huang, X., Chen, X. C., Theodorou, A., Hernández-Orallo, J., Castillo-Effen, M., . . . McDermid, J. (2023). The AAAI-2023 Workshop on Artificial Intelligence Safety (SafeAI-2023). In CEUR Workshop Proceedings Vol. 3381.
Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation
Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (2023). Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2023-May (pp. 1421-1429).
Learning by Analogy: Diverse Questions Generation in Math Word Problem
Zhou, Z., Ning, M., Wang, Q., Yao, J., Wang, W., Huang, X., & Huang, K. (2023). Learning by Analogy: Diverse Questions Generation in Math Word Problem. In Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics. doi:10.18653/v1/2023.findings-acl.705
Machine Learning Safety
Huang, X., Jin, G., & Ruan, W. (2023). Machine Learning Safety. Springer Nature Singapore. doi:10.1007/978-981-19-6814-3
Model-Agnostic Reachability Analysis on Deep Neural Networks
Zhang, C., Ruan, W., Wang, F., Xu, P., Min, G., & Huang, X. (2023). Model-Agnostic Reachability Analysis on Deep Neural Networks. In Unknown Conference (pp. 341-354). Springer Nature Switzerland. doi:10.1007/978-3-031-33374-3_27
Randomized Adversarial Training via Taylor Expansion
Jin, G., Yi, X., Wu, D., Mu, R., & Huang, X. (2023). Randomized Adversarial Training via Taylor Expansion. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. doi:10.1109/cvpr52729.2023.01578
The IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023)
Pedroza, G., Chen, X. C., Hernández-Orallo, J., Huang, X., Theodorou, A., Matragkas, N., . . . Liu, A. (2023). The IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023). In CEUR Workshop Proceedings Vol. 3505.
Weight-based Semantic Testing Approach for Deep Neural Networks
Alshareef, A., Berthier, N., Schewe, S., & Huang, X. (2023). Weight-based Semantic Testing Approach for Deep Neural Networks. In CEUR Workshop Proceedings Vol. 3505.
2022
Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion
Ma, W., Yang, X., Wang, Q., Huang, K., & Huang, X. (2022). Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion. CELLS, 11(24). doi:10.3390/cells11244107
Formal Specification for Learning-Enabled Autonomous Systems
Bensalem, S., Cheng, C. -H., Huang, X., Katsaros, P., Molin, A., Nickovic, D., & Peled, D. (2022). Formal Specification for Learning-Enabled Autonomous Systems. In Unknown Conference (pp. 131-143). Springer International Publishing. doi:10.1007/978-3-031-21222-2_8
Multi-Scope Feature Extraction for Point Cloud Completion
Ma, W., Wang, Q. -F., Huang, K., & Huang, X. (2022). Multi-Scope Feature Extraction for Point Cloud Completion. In 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) (pp. 727-732). IEEE. doi:10.1109/iccsi55536.2022.9970616
Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation
Liu, G., Huang, X., & Yi, X. (2022). Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation. In Unknown Conference (pp. 227-243). Springer Nature Switzerland. doi:10.1007/978-3-031-20065-6_14
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dong, Y., Zhao, X., & Huang, X. (2022). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. In 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 5171-5178). doi:10.1109/IROS47612.2022.9981794
STUN: Self-Teaching Uncertainty Estimation for Place Recognition
Cai, K., Lu, C. X., & Huang, X. (2022). STUN: Self-Teaching Uncertainty Estimation for Place Recognition. In 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 6614-6621). doi:10.1109/IROS47612.2022.9981546
Dependable learning-enabled multiagent systems
Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI COMMUNICATIONS, 35(4), 407-420. doi:10.3233/AIC-220128
Comparative study of modern heuristic algorithms for global maximum power point tracking in photovoltaic systems under partial shading conditions
Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2022). Comparative study of modern heuristic algorithms for global maximum power point tracking in photovoltaic systems under partial shading conditions. FRONTIERS IN ENERGY RESEARCH, 10. doi:10.3389/fenrg.2022.946864
Editorial to theme section on open environmental software systems modeling
Yue, T., Arcaini, P., Wu, J., & Huang, X. (2022). Editorial to theme section on open environmental software systems modeling. SOFTWARE AND SYSTEMS MODELING, 21(4), 1273-1275. doi:10.1007/s10270-022-01032-x
Short-term Load Forecasting with Distributed Long Short-Term Memory
Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2022). Short-term Load Forecasting with Distributed Long Short-Term Memory. Retrieved from http://arxiv.org/abs/2208.01147v2
Quantifying safety risks of deep neural networks
Xu, P., Ruan, W., & Huang, X. (2022). Quantifying safety risks of deep neural networks. Complex and Intelligent Systems. doi:10.1007/s40747-022-00790-x
Soft pseudo-Label shrinkage for unsupervised domain adaptive person re-identification
Zheng, D., Xiao, J., Chen, K., Huang, X., Chen, L., & Zhao, Y. (2022). Soft pseudo-Label shrinkage for unsupervised domain adaptive person re-identification. PATTERN RECOGNITION, 127. doi:10.1016/j.patcog.2022.108615
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dong, Y., Zhao, X., & Huang, X. (2021). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. Retrieved from http://dx.doi.org/10.1109/IROS47612.2022.9981794
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems
Qi, Y., Conmy, P. R., Huang, W., Zhao, X., & Huang, X. (2022). A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. Retrieved from http://arxiv.org/abs/2206.10216v1
A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network.
Wu, D., Yi, X., & Huang, X. (2022). A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network.. Frontiers in neuroscience, 16, 759900. doi:10.3389/fnins.2022.759900
Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation
Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qiao, Y., MacCormick, I. J. C., . . . Zheng, Y. (2022). Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING, 41(3), 690-701. doi:10.1109/TMI.2021.3123567
A Graph Neural Network Reasoner for Game Description Language
Gunawan, A., Ruan, J., & Huang, X. (2022). A Graph Neural Network Reasoner for Game Description Language. In Proceedings of the Nineteenth International Conference on Principles of Knowledge Representation and Reasoning (pp. 443-452). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/kr.2022/46
A Graph Neural Network Reasoner for Game Description Language
Gunawan, A., Ruan, J., & Huang, X. (2022). A Graph Neural Network Reasoner for Game Description Language. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 3 (pp. 1607-1609).
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems
Qi, Y., Conmy, P. R., Huang, W., Zhao, X., & Huang, X. (2022). A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. In CEUR Workshop Proceedings Vol. 3215.
Bridging Formal Methods and Machine Learning with Global Optimisation
Huang, X., Ruan, W., Tang, Q., & Zhao, X. (2022). Bridging Formal Methods and Machine Learning with Global Optimisation. In Lecture Notes in Computer Science (pp. 1-19). Springer International Publishing. doi:10.1007/978-3-031-17244-1_1
Enhancing Adversarial Training with Second-Order Statistics of Weights
Jin, G., Yi, X., Huang, W., Schewe, S., & Huang, X. (2022). Enhancing Adversarial Training with Second-Order Statistics of Weights. In 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (pp. 15252-15262). doi:10.1109/CVPR52688.2022.01484
EnnCore: End-to-End Conceptual Guarding of Neural Architectures
Manino, E., Carvalho, D., Dong, Y., Rozanova, J., Song, X., Mustafa, M. A., . . . Cordeiro, L. (2022). EnnCore: End-to-End Conceptual Guarding of Neural Architectures. In CEUR Workshop Proceedings Vol. 3087.
Quantifying the Importance of Latent Features in Neural Networks
Alshareef, A., Berthier, N., Schewe, S., & Huang, X. (2022). Quantifying the Importance of Latent Features in Neural Networks. In CEUR Workshop Proceedings Vol. 3087.
The AAAI-22 Workshop on Artificial Intelligence Safety (SafeAI 2022)
Pedroza, G., Hernández-Orallo, J., Chen, X. C., Huang, X., Espinoza, H., Castillo-Effen, M., . . . ÓhÉigeartaigh, S. S. (2022). The AAAI-22 Workshop on Artificial Intelligence Safety (SafeAI 2022). In CEUR Workshop Proceedings Vol. 3087.
The IJCAI-ECAI-22 Workshop on Artificial Intelligence Safety
Pedroza, G., Chen, X. C., Hernández-Orallo, J., Huang, X., Espinoza, H., Mallah, R., . . . Castillo-Effen, M. (2022). The IJCAI-ECAI-22 Workshop on Artificial Intelligence Safety. In CEUR Workshop Proceedings Vol. 3215.
2021
Spatio-Clock Synchronous Constraint Guided Safe Reinforcement Learning for Autonomous Driving
Wang, J., Huang, Z., Yang, D., Huang, X., Zhu, Y., & Hua, G. (2021). Spatio-Clock Synchronous Constraint Guided Safe Reinforcement Learning for Autonomous Driving. Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 58(12), 2585-2603. doi:10.7544/issn1000-1239.2021.20211023
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Dong, Y., Huang, W., Bharti, V., Cox, V., Banks, A., Wang, S., . . . Huang, X. (2023). Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 22(3). doi:10.1145/3570918
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
Ruan, W., Yi, X., & Huang, X. (2021). Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 4866-4869). ACM. doi:10.1145/3459637.3482029
Sim-to-Real Deep Reinforcement Learning for Maximum Power Point Tracking of Photovoltaic Systems
Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2021). Sim-to-Real Deep Reinforcement Learning for Maximum Power Point Tracking of Photovoltaic Systems. In 2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE). doi:10.1109/EEEIC/ICPSEurope51590.2021.9584821
Sim-to-Real Transfer with Domain Randomization for Maximum Power Point Estimation of Photovoltaic Systems
Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2021). Sim-to-Real Transfer with Domain Randomization for Maximum Power Point Estimation of Photovoltaic Systems. In 2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE). doi:10.1109/EEEIC/ICPSEurope51590.2021.9584526
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qian, X., Huang, X., & Zheng, Y. (2021). Spatial Uncertainty-Aware Semi-Supervised Crowd Counting. In 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (pp. 15529-15539). doi:10.1109/ICCV48922.2021.01526
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
Zhao, X., Huang, W., Banks, A., Cox, V., Flynn, D., Schewe, S., & Huang, X. (2021). Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles. Retrieved from http://arxiv.org/abs/2106.01258v1
Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation
Yang, P., Li, J., Liu, J., Huang, C. -C., Li, R., Chen, L., . . . Zhang, L. (2021). Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation. FORMAL ASPECTS OF COMPUTING, 33(3), 407-435. doi:10.1007/s00165-021-00548-1
Safety and reliability of deep learning
Huang, X. (2021). Safety and reliability of deep learning. In Proceedings of the 1st International Workshop on Verification of Autonomous & Robotic Systems. ACM. doi:10.1145/3459086.3459636
Detecting Operational Adversarial Examples for Reliable Deep Learning
Zhao, X., Huang, W., Schewe, S., Dong, Y., & Huang, X. (2021). Detecting Operational Adversarial Examples for Reliable Deep Learning. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS - SUPPLEMENTAL VOL (DSN 2021) (pp. 5-6). doi:10.1109/DSN-S52858.2021.00013
A Segment-Based Layout Aware Model for Information Extraction on Document Images
Ning, M., Wang, Q. -F., Huang, K., & Huang, X. (2021). A Segment-Based Layout Aware Model for Information Extraction on Document Images. In Unknown Conference (pp. 757-765). Springer International Publishing. doi:10.1007/978-3-030-92307-5_88
An Overview of Verification and Validation Challenges for Inspection Robots
Fisher, M., Cardoso, R. C., Collins, E. C., Dadswell, C., Dennis, L. A., Dixon, C., . . . Webster, M. (2021). An Overview of Verification and Validation Challenges for Inspection Robots. ROBOTICS, 10(2). doi:10.3390/robotics10020067
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Meng, Y., Zhang, H., Gao, D., Zhao, Y., Yang, X., Qian, X., . . . Zheng, Y. (2021). BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation. In 32nd British Machine Vision Conference, BMVC 2021.
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2021). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. In Proceedings of Machine Learning Research Vol. 161 (pp. 408-418).
Statistical Certification of Acceptable Robustness for Neural Networks
Huang, C., Hu, Z., Huang, X., & Pei, K. (2021). Statistical Certification of Acceptable Robustness for Neural Networks. In Unknown Conference (pp. 79-90). Springer International Publishing. doi:10.1007/978-3-030-86362-3_7
The AAAI-21 workshop on artificial intelligence safety (safeai 2021)
Espinoza, H., Hernández-Orallo, J., Chen, X. C., ÓhÉigeartaigh, S. S., Huang, X., Castillo-Effen, M., . . . McDermid, J. (2021). The AAAI-21 workshop on artificial intelligence safety (safeai 2021). In CEUR Workshop Proceedings Vol. 2808.
The IJCAI-21 Workshop on Artificial Intelligence Safety (AISafety2021)
Espinoza, H., Pedroza, G., Hernández-Orallo, J., Chen, X. C., ÓhÉigeartaigh, S. S., Huang, X., . . . McDermid, J. (2021). The IJCAI-21 Workshop on Artificial Intelligence Safety (AISafety2021). In CEUR Workshop Proceedings Vol. 2916.
2020
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2020). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. Retrieved from http://arxiv.org/abs/2012.03058v5
Multiclock Constraint System Modelling and Verification for Ensuring Cooperative Autonomous Driving Safety
Wang, J., Huang, Z., Huang, X., Zhu, Y., & Wang, F. (2020). Multiclock Constraint System Modelling and Verification for Ensuring Cooperative Autonomous Driving Safety. JOURNAL OF ADVANCED TRANSPORTATION, 2020. doi:10.1155/2020/8830752
The Association for the Advancement of Artificial Intelligence 2020 Workshop Program
Bang, G., Barash, G., Bea, R., Cali, J., Castillo-Effen, M., Chen, X. C., . . . Zhang, J. (2020). The Association for the Advancement of Artificial Intelligence 2020 Workshop Program. AI MAGAZINE, 41(4), 100-114. Retrieved from https://www.webofscience.com/
PRODeep: a platform for robustness verification of deep neural networks
Li, R., Li, J., Huang, C. -C., Yang, P., Huang, X., Zhang, L., . . . Hermanns, H. (2020). PRODeep: a platform for robustness verification of deep neural networks. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering Vol. 11822 (pp. 1630-1634). ACM. doi:10.1145/3368089.3417918
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
Zhang, Y., Ruan, W., Wang, F., & Huang, X. (2020). Generalizing Universal Adversarial Attacks Beyond Additive Perturbations. In 20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020) (pp. 1412-1417). doi:10.1109/ICDM50108.2020.00186
Practical Verification of Neural Network Enabled State Estimation System for Robotics
Huang, W., Zhou, Y., Sun, Y., Sharp, J., Maskell, S., & Huang, X. (2020). Practical Verification of Neural Network Enabled State Estimation System for Robotics. In 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 7336-7343). doi:10.1109/IROS45743.2020.9340720
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
Huang, W., Zhao, X., & Huang, X. (n.d.). Embedding and Extraction of Knowledge in Tree Ensemble Classifiers. Machine Learning. Retrieved from http://arxiv.org/abs/2010.08281v2
How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks
Jin, G., Yi, X., Zhang, L., Zhang, L., Schewe, S., & Huang, X. (2020). How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks. Retrieved from http://arxiv.org/abs/2010.05983v3
Adaptable and Verifiable BDI Reasoning*
Stringer, P., Cardoso, R. C., Huang, X., & Dennis, L. A. (2020). Adaptable and Verifiable BDI Reasoning*. In ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE (pp. 117-125). doi:10.4204/EPTCS.319.9
Adaptable and Verifiable BDI Reasoning
Generating Adversarial Inputs Using A Black-box Differential Technique
Juúnior, J. B. P. M., Cordeiro, L. C., d'Amorim, M., & Huang, X. (2020). Generating Adversarial Inputs Using A Black-box Differential Technique. Retrieved from http://arxiv.org/abs/2007.05315v1
A Safety Framework for Critical Systems Utilising Deep Neural Networks
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M., & Huang, X. (2020). A Safety Framework for Critical Systems Utilising Deep Neural Networks. Retrieved from http://dx.doi.org/10.1007/978-3-030-54549-9_16
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
Wu, M., Wicker, M., Ruan, W., Huang, X., & Kwiatkowska, M. (2020). A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees. Theoretical Computer Science, 807, 298-329. doi:10.1016/j.tcs.2019.05.046
Reliability Validation of Learning Enabled Vehicle Tracking
Sun, Y., Zhou, Y., Maskell, S., Sharp, J., & Huang, X. (2020). Reliability Validation of Learning Enabled Vehicle Tracking. 2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 9390-9396. doi:10.1109/icra40945.2020.9196932
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M., & Huang, X. (2020). A Safety Framework for Critical Systems Utilising Deep Neural Networks. In Computer Safety, Reliability, and Security (Vol. 12234, pp. 244-259). Springer Nature. doi:10.1007/978-3-030-54549-9_16
CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation
Meng, Y., Wei, M., Gao, D., Zhao, Y., Yang, X., Huang, X., & Zheng, Y. (2020). CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation. In Unknown Conference (pp. 352-362). Springer International Publishing. doi:10.1007/978-3-030-59719-1_35
Explaining Image Classifiers Using Statistical Fault Localization
Sun, Y., Chockler, H., Huang, X., & Kroening, D. (2020). Explaining Image Classifiers Using Statistical Fault Localization. In Computer Vision – ECCV 2020 (Vol. 12373, pp. 391-406). Springer Nature. doi:10.1007/978-3-030-58604-1_24
Regression of Instance Boundary by Aggregated CNN and GCN
Meng, Y., Meng, W., Gao, D., Zhao, Y., Yang, X., Huang, X., & Zheng, Y. (2020). Regression of Instance Boundary by Aggregated CNN and GCN. In Unknown Conference (pp. 190-207). Springer International Publishing. doi:10.1007/978-3-030-58598-3_12
The AAAI-20 workshop on artificial intelligence safety (SafeAI 2020)
Espinoza, H., Hernández-Orallo, J., Chen, X. C., ÓhÉigeartaigh, S. S., Huang, X., Castillo-Effen, M., . . . McDermid, J. (2020). The AAAI-20 workshop on artificial intelligence safety (SafeAI 2020). In CEUR Workshop Proceedings Vol. 2560 (pp. I-IV).
The IJCAI-PRICAI-20 workshop on artificial intelligence safety (AISafety 2020)
Espinoza, H., McDermid, J., Huang, X., Castillo-Effen, M., Chen, X. C., Hernández-Orallo, J., . . . Mallah, R. (2020). The IJCAI-PRICAI-20 workshop on artificial intelligence safety (AISafety 2020). In CEUR Workshop Proceedings Vol. 2640.
2019
Coverage-Guided Testing for Recurrent Neural Networks
Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J., & Huang, X. (2022). Coverage-Guided Testing for Recurrent Neural Networks. IEEE TRANSACTIONS ON RELIABILITY, 71(3), 1191-1206. doi:10.1109/TR.2021.3080664
Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles
Wu, M., Louw, T., Lahijanian, M., Ruan, W., Huang, X., Merat, N., & Kwiatkowska, M. (2019). Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles. In 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 6210-6216). doi:10.1109/iros40897.2019.8967779
Reasoning about Cognitive Trust in Stochastic Multiagent Systems
Huang, X. H., Kwiatkowska, M., & Olejnik, M. (2019). Reasoning about Cognitive Trust in Stochastic Multiagent Systems. ACM Transactions on Computational Logic, 20(04), 64 pages. doi:10.1145/3329123
Structural Test Coverage Criteria for Deep Neural Networks
Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2019). Structural Test Coverage Criteria for Deep Neural Networks. ACM Transactions on Embedded Computing Systems, 18(5S). doi:10.1145/3358233
Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management
Zhao, X., Osborne, M., Lantair, J., Robu, V., Flynn, D., Huang, X., . . . Ferrando, A. (2019). Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management. In SOFTWARE ENGINEERING AND FORMAL METHODS (SEFM 2019) Vol. 11724 (pp. 105-124). doi:10.1007/978-3-030-30446-1_6
Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management
Explaining Image Classifiers using Statistical Fault Localization
Sun, Y., Chockler, H., Huang, X., & Kroening, D. (2019). Explaining Image Classifiers using Statistical Fault Localization. Retrieved from http://arxiv.org/abs/1908.02374v2
Explaining Image Classifiers using Statistical Fault Localization
Reasoning about Cognitive Trust in Stochastic Multiagent Systems
Huang, X., & Kwiatkowska, M. (2017). Reasoning about Cognitive Trust in Stochastic Multiagent Systems. In THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 3768-3774). Retrieved from https://www.webofscience.com/
Reasoning about Cognitive Trust in Stochastic Multiagent Systems
DeepConcolic: Testing and Debugging Deep Neural Networks
Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2019). DeepConcolic: Testing and Debugging Deep Neural Networks. In 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2019) (pp. 111-114). doi:10.1109/ICSE-Companion.2019.00051
Structural Test Coverage Criteria for Deep Neural Networks
Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2019). Structural Test Coverage Criteria for Deep Neural Networks. 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2019), 320-321. doi:10.1109/ICSE-Companion.2019.00134
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification
Li, J., Liu, J., Yang, P., Chen, L., Huang, X., & Zhang, L. (2019). Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification. In Unknown Conference (pp. 296-319). Springer International Publishing. doi:10.1007/978-3-030-32304-2_15
Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence
Barash, G., Castillo-Effen, M., Chhaya, N., Clark, P., Espinoza, H., Farchi, E., . . . Zitouni, I. (2019). Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence. In AI MAGAZINE Vol. 40 (pp. 67-78). doi:10.1609/aimag.v40i3.4981
The AAAI-19 workshop on artificial intelligence safety (SafeAI 2019)
Espinoza, H., hÉigeartaigh, S., Huang, X., Hernández-Orallo, J., & Castillo-Effen, M. (2019). The AAAI-19 workshop on artificial intelligence safety (SafeAI 2019). In CEUR Workshop Proceedings Vol. 2301.
The IJCAI-19 workshop on artificial intelligence safety (AI Safety 2019)
Espinoza, H., Yu, H., Huang, X., Lecue, F., Chen, C., Hernández-Orallo, J., . . . Mallah, R. (2019). The IJCAI-19 workshop on artificial intelligence safety (AI Safety 2019). In CEUR Workshop Proceedings Vol. 2419.
Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management.
Zhao, X., Osborne, M., Lantair, J., Robu, V., Flynn, D., Huang, X., . . . Ferrando, A. (2019). Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management.. In P. C. Ölveczky, & G. Salaün (Eds.), SEFM Vol. 11724 (pp. 105-124). Springer. Retrieved from https://doi.org/10.1007/978-3-030-30446-1
2018
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability?
Huang, X., Kroening, D., Ruan, W., Sharp, J., Sun, Y., Thamo, E., . . . Yi, X. (2020). A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability?. COMPUTER SCIENCE REVIEW, 37. doi:10.1016/j.cosrev.2020.100270
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
An Epistemic Strategy Logic
Huang, X., & Meyden, R. V. D. (2018). An Epistemic Strategy Logic. ACM Transactions on Computational Logic, 19(4). doi:10.1145/3233769
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Ruan, W., Huang, X., & Kwiatkowska, M. (2018). Reachability Analysis of Deep Neural Networks with Provable Guarantees. Retrieved from http://arxiv.org/abs/1805.02242v1
Concolic Testing for Deep Neural Networks
Concolic Testing for Deep Neural Networks
Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M., & Kroening, D. (2018, September 3). Concolic Testing for Deep Neural Networks. In 33rd IEEE/ACM International Conference on Automated Software Engineering. Montpellier, France. Retrieved from http://arxiv.org/abs/1805.00089v1
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm
Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D., & Kwiatkowska, M. (2018). Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm. Retrieved from http://arxiv.org/abs/1804.05805v2
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm
Testing Deep Neural Networks
Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2018). Testing Deep Neural Networks. Retrieved from http://arxiv.org/abs/1803.04792v4
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
Wicker, M., Huang, X., & Kwiatkowska, M. (2018). Feature-Guided Black-Box Safety Testing of Deep Neural Networks. In Unknown Conference (pp. 408-426). Springer International Publishing. doi:10.1007/978-3-319-89960-2_22
Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems
Fu, C., Turrini, A., Huang, X., Song, L., Feng, Y., & Zhang, L. (2018). Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems. In PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 4757-4763). Retrieved from https://www.webofscience.com/
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Ruan, W., Huang, X., & Kwiatkowska, M. (2018). Reachability Analysis of Deep Neural Networks with Provable Guarantees. In PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 2651-2659). Retrieved from https://www.webofscience.com/
2017
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
Wicker, M., Huang, X., & Kwiatkowska, M. (2017). Feature-Guided Black-Box Safety Testing of Deep Neural Networks. Retrieved from http://arxiv.org/abs/1710.07859v2
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
ATL Strategic Reasoning Meets Correlated Equilibrium
Huang, X., & Ruan, J. (2017). ATL Strategic Reasoning Meets Correlated Equilibrium. In PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 1102-1108). Retrieved from https://www.webofscience.com/
Quantified Coalition Logic of Knowledge, Belief and Certainty
Chen, Q., Huang, X., Su, K., & Sattar, A. (2017). Quantified Coalition Logic of Knowledge, Belief and Certainty. In ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2017 Vol. 10233 (pp. 351-360). doi:10.1007/978-3-319-57351-9_40
Reasoning about cognitive trust in stochastic multiagent systems
Huang, X., & Kwiatkowska, M. (2017). Reasoning about cognitive trust in stochastic multiagent systems. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 3768-3774).
2016
Safety Verification of Deep Neural Networks
Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2017). Safety Verification of Deep Neural Networks. In Computer Aided Verification Vol. 10426 (pp. 3-29). Heidelberg, Germany. doi:10.1007/978-3-319-63387-9_1
Normative Multiagent Systems: A Dynamic Generalization
Huang, X., Ruan, J., Chen, Q., & Su, K. (2016). Normative Multiagent Systems: A Dynamic Generalization. Retrieved from http://arxiv.org/abs/1604.05086v1
The complexity of approximations for epistemic synthesis (extended abstract)
Huang, X., & van der Meyden, R. (n.d.). The complexity of approximations for epistemic synthesis (extended abstract). In Electronic Proceedings in Theoretical Computer Science Vol. 202 (pp. 120-137). Open Publishing Association. doi:10.4204/eptcs.202.9
The complexity of approximations for epistemic synthesis (extended abstract)
Model checking probabilistic knowledge: A PSPACE case
Huang, X., & Kwiatkowska, M. (2016). Model checking probabilistic knowledge: A PSPACE case. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2516-2522).
Normative multiagent systems: A dynamic generalization
Huang, X., Ruan, J., Chen, Q., & Su, K. (2016). Normative multiagent systems: A dynamic generalization. IJCAI International Joint Conference on Artificial Intelligence, 2016-January, 1123-1129.
Reconfigurability in reactive multiagent systems
Huang, X., Chen, Q., Meng, J., & Su, K. (2016). Reconfigurability in reactive multiagent systems. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2016-January (pp. 315-321).
Safety Verification of Deep Neural Networks
Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2016). Safety Verification of Deep Neural Networks. CoRR, abs/1610.06940. Retrieved from http://arxiv.org/abs/1610.06940
Strengthening agents strategic ability with communication
Huang, X., Chen, Q., & Su, K. (2016). Strengthening agents strategic ability with communication. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2509-2515).
2015
Bounded model checking of strategy ability with perfect recall
Huang, X. (2015). Bounded model checking of strategy ability with perfect recall. Artificial Intelligence, 222, 182-200. doi:10.1016/j.artint.2015.01.005
The Complexity of Model Checking Succinct Multiagent Systems
Huang, X., Chen, Q., & Su, K. (2015). The Complexity of Model Checking Succinct Multiagent Systems. In PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI) (pp. 1076-1082). Retrieved from https://www.webofscience.com/
2014
An Epistemic Strategy Logic
Huang, X., & Meyden, R. V. D. (2014). An Epistemic Strategy Logic. Retrieved from http://arxiv.org/abs/1409.2193v3
An Epistemic Strategy Logic (Extended Abstract)
Huang, X., & van der Meyden, R. (n.d.). An Epistemic Strategy Logic (Extended Abstract). In Electronic Proceedings in Theoretical Computer Science Vol. 146 (pp. 35-41). Open Publishing Association. doi:10.4204/eptcs.146.5
A Temporal Logic of Strategic Knowledge
Huang, X., & van der Meyden, R. (2014). A Temporal Logic of Strategic Knowledge. In FOURTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING (pp. 418-427). Retrieved from https://www.webofscience.com/
Symbolic Model Checking Epistemic Strategy Logic
Huang, X., & Van der Meyden, R. (2014). Symbolic Model Checking Epistemic Strategy Logic. In PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 1426-1432). Retrieved from https://www.webofscience.com/
Symbolic Synthesis for Epistemic Specifications with Observational Semantics
Huang, X., & van der Meyden, R. (2014). Symbolic Synthesis for Epistemic Specifications with Observational Semantics. In Unknown Conference (pp. 455-469). Springer Berlin Heidelberg. doi:10.1007/978-3-642-54862-8_39
2013
Model Checking for Reasoning about Incomplete Information Games
Huang, X., Ruan, J., & Thielscher, M. (2013). Model Checking for Reasoning about Incomplete Information Games. In Unknown Conference (pp. 246-258). Springer International Publishing. doi:10.1007/978-3-319-03680-9_27
Symbolic Synthesis of Knowledge-based Program Implementations with Synchronous Semantics
Huang, X., & Meyden, R. V. D. (2013). Symbolic Synthesis of Knowledge-based Program Implementations with Synchronous Semantics. Retrieved from http://arxiv.org/abs/1310.6423v1
A logic of probabilistic knowledge and strategy
Huang, X., & Luo, C. (2013). A logic of probabilistic knowledge and strategy. In 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 Vol. 2 (pp. 845-852).
Bounded planning for strategic goals with incomplete information and perfect recall
Huang, X. (2013). Bounded planning for strategic goals with incomplete information and perfect recall. In 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 Vol. 2 (pp. 885-892).
Diagnosability in concurrent probabilistic systems
Huang, X. (2013). Diagnosability in concurrent probabilistic systems. In 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 Vol. 2 (pp. 853-860).
Symbolic synthesis of knowledge-based program implementations with synchronous semantics
Huang, X., & Van Der Meyden, R. (2013). Symbolic synthesis of knowledge-based program implementations with synchronous semantics. Proceedings of the 14th Conference on Theoretical Aspects of Rationality and Knowledge, TARK 2013, 121-130.
2012
Probabilistic alternating-time temporal logic of incomplete information and synchronous perfect recall
Huang, X., Su, K., & Zhang, C. (2012). Probabilistic alternating-time temporal logic of incomplete information and synchronous perfect recall. In Proceedings of the National Conference on Artificial Intelligence Vol. 1 (pp. 765-771).
Synthesizing strategies for epistemic goals by epistemic model checking: An application to pursuit evasion games
Huang, X., & Van Der Meyden, R. (2012). Synthesizing strategies for epistemic goals by epistemic model checking: An application to pursuit evasion games. In Proceedings of the National Conference on Artificial Intelligence Vol. 1 (pp. 772-778).
Probabilistic Alternating-Time Temporal Logic of Incomplete Information and Synchronous Perfect Recall
Huang, X., Su, K., & Zhang, C. (2012). Probabilistic Alternating-Time Temporal Logic of Incomplete Information and Synchronous Perfect Recall. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 765-771).
Synthesizing Strategies for Epistemic Goals by Epistemic Model Checking: An Application to Pursuit Evasion Games
Huang, X., & van der Meyden, R. (2012). Synthesizing Strategies for Epistemic Goals by Epistemic Model Checking: An Application to Pursuit Evasion Games. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 772-778).
2011
Model checking knowledge in pursuit evasion games
Huang, X., Maupin, P., & Van Der Meyden, R. (2011). Model checking knowledge in pursuit evasion games. In IJCAI International Joint Conference on Artificial Intelligence (pp. 240-245). doi:10.5591/978-1-57735-516-8/IJCAI11-051
Symbolic model checking of probabilistic knowledge
Huang, X., Luo, C., & van der Meyden, R. (2011). Symbolic model checking of probabilistic knowledge. In Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge Vol. 3308 (pp. 177-186). ACM. doi:10.1145/2000378.2000399
Improved Bounded Model Checking for a Fair Branching-Time Temporal Epistemic Logic
Huang, X., Luo, C., & van der Meyden, R. (2011). Improved Bounded Model Checking for a Fair Branching-Time Temporal Epistemic Logic. In Unknown Conference (pp. 95-111). Springer Berlin Heidelberg. doi:10.1007/978-3-642-20674-0_7
2010
A precongruence format for should testing preorder
Huang, X., Jiao, L., & Lu, W. (2010). A precongruence format for should testing preorder. JOURNAL OF LOGIC AND ALGEBRAIC PROGRAMMING, 79(3-5), 245-263. doi:10.1016/j.jlap.2010.03.001
Congruence Formats for Weak Readiness Equivalence and Weak Possible Future Equivalence
Huang, X., Jiao, L., & Lu, W. (2010). Congruence Formats for Weak Readiness Equivalence and Weak Possible Future Equivalence. COMPUTER JOURNAL, 53(1), 21-36. doi:10.1093/comjnl/bxn009
Improved bounded model checking for a fair branching-time temporal epistemic logic
Huang, X., Luo, C., & Meyden, R. V. D. (2010). Improved bounded model checking for a fair branching-time temporal epistemic logic. In 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10-14, 2010, Volume 1-3 (pp. 1403-1404). doi:10.1145/1838206.1838403
The complexity of epistemic model checking: Clock semantics and branching time
Huang, X., & Van Der Meyden, R. (2010). The complexity of epistemic model checking: Clock semantics and branching time. In Frontiers in Artificial Intelligence and Applications Vol. 215 (pp. 549-554). doi:10.3233/978-1-60750-606-5-549
2009
Model Checking Games for a Fair Branching-Time Temporal Epistemic Logic
Huang, X., & van der Meyden, R. (2009). Model Checking Games for a Fair Branching-Time Temporal Epistemic Logic. In Unknown Conference (pp. 11-20). Springer Berlin Heidelberg. doi:10.1007/978-3-642-10439-8_2
2008
Weak Parametric Failure Equivalences and Their Congruence Formats
Huang, X., Jiao, L., & Lu, W. (2008). Weak Parametric Failure Equivalences and Their Congruence Formats. In Theory of Computing 2008. Proc. Fourteenth Computing: The Australasian Theory Symposium (CATS 2008), Wollongong, NSW, Australia, January 22-25, 2008. Proceedings (pp. 15-26). Retrieved from http://crpit.com/abstracts/CRPITV77Huang.html
2007
A Semantic Preorder on Refinement and Fairness
Huang, X. W., Jiao, L., & Lu, W. M. (2007). A Semantic Preorder on Refinement and Fairness. In First Joint IEEE/IFIP Symposium on Theoretical Aspects of Software Engineering (TASE '07) Vol. 31 (pp. 139-148). IEEE. doi:10.1109/tase.2007.5
A Modular Petri Net Used in Synchronous Communication of Sequential Processes
Huang, X., & Meng, J. (2007). A Modular Petri Net Used in Synchronous Communication of Sequential Processes. In Proceedings of the 2007 International Conference on Modeling, Simulation & Visualization Methods, MSV 2007, Las Vegas, Nevada, USA, June 25-28, 2007 (pp. 194-200).
A Semantic Preorder Combining ST Notion and Fair Testing Semantic
Huang, X., & Meng, J. (2007). A Semantic Preorder Combining ST Notion and Fair Testing Semantic. In Proceedings of the 2007 International Conference on Foundations of Computer Science, FCS 2007, June 25-28, 2007, Las Vegas, Nevada, USA (pp. 82-88).
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