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Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework
Published in 2021 IEEE Intelligent Vehicles Symposium (IV), 2021
This paper presents an efficient dual cyber-physical blockchain framework for securing connected vehicle applications, addressing challenges in road transportation and intelligent vehicle systems.
Recommended citation: Liu, X., Luo, B., Abdo, A., Abu-Ghazaleh, N., & Zhu, Q. (2021). "Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework." 2021 IEEE Intelligent Vehicles Symposium (IV). 393-400.
Paper
Credibility Enhanced Temporal Graph Convolutional Network Based Sybil Attack Detection On Edge Computing Servers
Published in 2021 IEEE Intelligent Vehicles Symposium (IV), 2021
This paper presents a Credibility Enhanced Temporal Graph Convolutional Network for detecting Sybil attacks on edge computing servers in vehicular ad hoc networks.
Recommended citation: Luo, B., Liu, X., & Zhu, Q. (2021). "Credibility Enhanced Temporal Graph Convolutional Network Based Sybil Attack Detection On Edge Computing Servers." 2021 IEEE Intelligent Vehicles Symposium (IV). 524-531.
Paper
Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems
Published in 2022 IEEE International Conference on Assured Autonomy (ICAA), 2022
This paper presents a risk-aware scene sampling approach for dynamic assurance of autonomous systems, addressing challenges in cyber-physical systems and uncertainty management.
Recommended citation: Ramakrishna, S., Luo, B., Barve, Y., Karsai, G., & Dubey, A. (2022). "Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems." 2022 IEEE International Conference on Assured Autonomy (ICAA). 107-116.
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ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA
Published in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022
This paper presents ANTI-CARLA, an adversarial testing framework for autonomous vehicles using the CARLA simulator.
Recommended citation: Ramakrishna, S., Luo, B., Kuhn, C. B., Karsai, G., & Dubey, A. (2022). "ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA." 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 2620-2627.
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Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems
Published in Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023), 2023
This paper proposes a dynamic simplex strategy with online controller switching logic for balancing safety and performance in autonomous cyber-physical systems.
Recommended citation: Luo, B., Ramakrishna, S., Pettet, A., Kuhn, C., Karsai, G., & Mukhopadhyay, A. (2023). "Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems." Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023). 177-186.
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Adaptive Decision-Making in Non-Stationary Markov Decision Processes
Published in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024
This research addresses the challenge of effective decision-making in non-stationary environments, focusing on “anytime” decision-making and adaptive strategies that balance performance optimization and safety prioritization.
Recommended citation: Luo, B. (2024). "Adaptive Decision-Making in Non-Stationary Markov Decision Processes." Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 2755-2757.
Paper
Decision Making in Non-Stationary Environments with Policy-Augmented Search
Published in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024
This paper introduces Policy-Augmented Monte Carlo tree search (PA-MCTS), which combines out-of-date policy estimates with online search using up-to-date environment models for decision-making in non-stationary environments.
Recommended citation: Pettet, A., Zhang, Y., Luo, B., Wray, K., Baier, H., Laszka, A., Dubey, A., & Mukhopadhyay, A. (2024). "Decision Making in Non-Stationary Environments with Policy-Augmented Search." Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 2417-2419.
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Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
Published in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024 Vanderbilt C.F. Chen Best Paper Runner-Up
This paper presents a heuristic search algorithm called Adaptive Monte Carlo Tree Search (ADA-MCTS) for decision-making in non-stationary Markov decision processes.
Recommended citation: Luo, B., Zhang, Y., Dubey, A., & Mukhopadhyay, A. (2024). "Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes." Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 1301-1309.
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Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
Published in IEEE International Conference on Assured Autonomy (ICAA 2024), 2024
This paper presents Shrinking POMCP, a framework for real-time UAV search and rescue built on partially observable Monte Carlo planning.
Recommended citation: Zhang, Y., Luo, B., Mukhopadhyay, A., Stojcsics, D., Elenius, D., Roy, A., Jha, S., Maroti, M., Koutsoukos, X., Karsai, G., & Dubey, A. (2024). "Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue." IEEE International Conference on Assured Autonomy (ICAA 2024).
Paper
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Published in The Thirteenth International Conference on Learning Representations (ICLR 2025), 2025 Spotlight (top 5.1%)
This paper introduces the Latent Macro Action Planner (L-MAP), which learns temporally extended macro-actions with a state-conditioned VQ-VAE and plans over them with Monte Carlo Tree Search, enabling low-latency decision-making in stochastic, high-dimensional continuous control.
Recommended citation: Luo, B., Pettet, A., Laszka, A., Dubey, A., & Mukhopadhyay, A. (2025). "Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction." The Thirteenth International Conference on Learning Representations (ICLR 2025).
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Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
Published in Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2025), 2025
This paper presents an annealed importance resampling approach for adapting to incoming observations in partially observable Markov decision processes.
Recommended citation: Zhang, Y., Luo, B., Mukhopadhyay, A., & Dubey, A. (2025). "Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes." Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2025).
Paper
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Published in Advances in Neural Information Processing Systems (NeurIPS 2025), 2025
This paper introduces ESCORT, a temporal belief representation for partially observable Markov decision processes that combines Stein variational inference with sliced consistency optimization for efficient belief tracking.
Recommended citation: Zhang, Y., Luo, B., Mukhopadhyay, A., Karsai, G., & Dubey, A. (2025). "ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs." Advances in Neural Information Processing Systems (NeurIPS 2025).
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NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes
Published in Advances in Neural Information Processing Systems (NeurIPS 2025), Datasets and Benchmarks Track, 2025
NS-Gym is an open-source toolkit of simulation environments and benchmarks for evaluating online decision-making algorithms in non-stationary Markov decision processes.
Recommended citation: Keplinger, N. S., Luo, B., Bektas, I., Zhang, Y., Wray, K. H., Laszka, A., Dubey, A., & Mukhopadhyay, A. (2025). "NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes." Advances in Neural Information Processing Systems (NeurIPS 2025), Datasets and Benchmarks Track.
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Towards Assured Autonomy using Neuro-Symbolic Components and Systems
Published in Neuro-symbolic AI: Foundations and Applications, Chapter 4 (Wiley, in press), 2026
Book chapter on building assured autonomous systems from neuro-symbolic components and systems.
Recommended citation: Dubey, A., Johnson, T. T., Koutsoukos, X., Luo, B., Manzanas Lopez, D., Maroti, M., Mukhopadhyay, A., Potteiger, N., Serbinowska, S., Stojcsics, D., Zhang, Y., & Karsai, G. (in press). "Towards Assured Autonomy using Neuro-Symbolic Components and Systems." In Neuro-symbolic AI: Foundations and Applications (Chap. 4). Wiley.