Search Results for author:
Found 15 papers, 5 papers with code
Safe POMDP Online Planning via Shielding
POMDP online planning algorithms such as Partially Observable Monte-Carlo Planning (POMCP) can solve very large POMDPs with the goal of maximizing the expected return.
CARNA: Characterizing Advanced heart failure Risk and hemodyNAmic phenotypes using learned multi-valued decision diagrams
To address these limitations, this paper presents CARNA, a hemodynamic risk stratification and phenotyping framework for advanced HF that takes advantage of the explainability and expressivity of machine learned Multi-Valued Decision Diagrams (MVDDs).
Explainable Multi-Agent Reinforcement Learning for Temporal Queries
As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments.
Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems
In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions.
Logic-based Reward Shaping for Multi-Agent Reinforcement Learning
We present a novel method for semi-centralized logic-based MARL reward shaping that is scalable in the number of agents and evaluate it in multiple scenarios.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Toward Policy Explanations for Multi-Agent Reinforcement Learning
Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving.
Multi-Objective Controller Synthesis with Uncertain Human Preferences
Complex real-world applications of cyber-physical systems give rise to the need for multi-objective controller synthesis, which concerns the problem of computing an optimal controller subject to multiple (possibly conflicting) criteria.
Safe Multi-Agent Reinforcement Learning via Shielding
Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e. g., no unsafe states are ever visited) during the learning process. Unfortunately, current MARL methods do not have safety guarantees.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data
Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements.
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
In this paper, we develop a new temporal logic-based learning framework, STLnet, which guides the RNN learning process with auxiliary knowledge of model properties, and produces a more robust model for improved future predictions.
Towards Personalized Explanation of Robot Path Planning via User Feedback
Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration.
Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets.
Towards Transparent Robotic Planning via Contrastive Explanations
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust.
MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning
Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations.
Counterexamples for Robotic Planning Explained in Structured Language
Automated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may help diagnose requirement violations.
