Learning Optimal Signal Temporal Logic Decision Trees for Classification: A Max-Flow MILP Formulation
Machine Learning
Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications
Learning Signal Temporal Logic through Neural Network for Interpretable Classification
Time-Incremental Learning of Temporal Logic Classifiers Using Decision Trees
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications
Learning A Risk-Aware Trajectory Planner From Demonstrations Using Logic Monitor
Differentiable Logic Layer for Rule Guided Trajectory Prediction
Learning An Explainable Trajectory Generator Using The Automaton Generative Network (AGN)
Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features
Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior
Brandon Araki, Kiran Vodrahalli, Thomas Leech, Cristian Ioan Vasile, Mark Donahue, and Daniela Rus. Deep Bayesian Non-parametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior. In AAAI Conference on Artificial Intellifence, New York, NY, USA, July 2020.
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