Search Results for author: Baiting Luo

Found 6 papers, 4 papers with code

Decision Making in Non-Stationary Environments with Policy-Augmented Search

1 code implementation6 Jan 2024 Ava Pettet, Yunuo Zhang, Baiting Luo, Kyle Wray, Hendrik Baier, Aron Laszka, Abhishek Dubey, Ayan Mukhopadhyay

In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.

Decision Making Decision Making Under Uncertainty +2

Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes

1 code implementation3 Jan 2024 Baiting Luo, Yunuo Zhang, Abhishek Dubey, Ayan Mukhopadhyay

However, existing approaches for decision-making in NSMDPs have two major shortcomings: first, they assume that the updated environmental dynamics at the current time are known (although future dynamics can change); and second, planning is largely pessimistic, i. e., the agent acts ``safely'' to account for the non-stationary evolution of the environment.

Decision Making

Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems

no code implementations20 Feb 2023 Baiting Luo, Shreyas Ramakrishna, Ava Pettet, Christopher Kuhn, Gabor Karsai, Ayan Mukhopadhyay

To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching.

Decision Making

ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

1 code implementation19 Jul 2022 Shreyas Ramakrishna, Baiting Luo, Christopher Kuhn, Gabor Karsai, Abhishek Dubey

A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system.

Autonomous Driving

Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

1 code implementation28 Feb 2022 Shreyas Ramakrishna, Baiting Luo, Yogesh Barve, Gabor Karsai, Abhishek Dubey

Our samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83% and 92%, compared to 56%, 66% and 71% of the grid, random and Halton samplers, respectively.

Bayesian Optimization Scene Generation

Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework

no code implementations15 Feb 2021 Xiangguo Liu, Baiting Luo, Ahmed Abdo, Nael Abu-Ghazaleh, Qi Zhu

While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating.

Cryptography and Security

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