Search Results for author:
Found 10 papers, 0 papers with code
Graph Q-Learning for Combinatorial Optimization
Graph-structured data is ubiquitous throughout natural and social sciences, and Graph Neural Networks (GNNs) have recently been shown to be effective at solving prediction and inference problems on graph data.
Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning
We advance the state of the art in Boolean composition of learned tasks with three contributions: i) introduce two distinct notions of safety in this framework; ii) show how to enforce either safety semantics, prove correctness (under some assumptions), and analyze the trade-offs between the two safety notions; and iii) extend Boolean composition from discrete action spaces to continuous action spaces.
STL: Surprisingly Tricky Logic (for System Validation)
The ground-truth validity of a specification, subjects' familiarity with formal methods, and subjects' level of education were found to be significant factors in determining validation correctness.
Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control
In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control.
CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications
In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under complex mission requirements from Capability Temporal Logic plus (CaTL+) specifications.
Robust Multi-Agent Coordination from CaTL+ Specifications
We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements.
Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications
This is achieved by decomposing an infeasible LTL formula into several reach-avoid sub-tasks with shorter horizons, which can be trained in a modular DRL architecture.
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models
In this work, we leverage contextual awareness for the anomaly detection problem.
Fast Decomposition of Temporal Logic Specifications for Heterogeneous Teams
In this work, we focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently.
Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0
20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings.
