50 citations · 80 across the 22 of their papers we have counts for
24 papers
Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine
Shayan Meshkat Alsadat, Jean-Raphael Gaglione, Daniel Neider +2
We present LARL-RM (Large language model-generated Automaton for Reinforcement Learning with Reward Machine) algorithm in order to encode high-level knowledge into reinforcement le…
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione +5
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a common approach is to represen…
Reinforcement Learning With Reward Machines in Stochastic Games
Jueming Hu, Jean-Raphael Gaglione, Yanze Wang +3
We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate h…
Learning Temporal Logic Properties: an Overview of Two Recent Methods
Jean-Raphaël Gaglione, Rajarshi Roy, Nasim Baharisangari +3
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior. We summarize two me…
Learning Interpretable Temporal Properties from Positive Examples Only
Rajarshi Roy, Jean-Raphaël Gaglione, Nasim Baharisangari +3
We consider the problem of explaining the temporal behavior of black-box systems using human-interpretable models. To this end, based on recent research trends, we rely on the fund…
Hierarchical Control for Cooperative Teams in Competitive Autonomous Racing
Rishabh Saumil Thakkar, Aryaman Singh Samyal, David Fridovich-Keil +2
We investigate the problem of autonomous racing among teams of cooperative agents that are subject to realistic racing rules. Our work extends previous research on hierarchical con…
Hierarchical Control for Head-to-Head Autonomous Racing
Rishabh Saumil Thakkar, Aryaman Singh Samyal, David Fridovich-Keil +2
We develop a hierarchical controller for head-to-head autonomous racing. We first introduce a formulation of a racing game with realistic safety and fairness rules. A high-level pl…
On the Detection of Markov Decision Processes
Xiaoming Duan, Yagiz Savas, Rui Yan +2
We study the detection problem for a finite set of Markov decision processes (MDPs) where the MDPs have the same state and action spaces but possibly different probabilistic transi…
Non-Parametric Neuro-Adaptive Coordination of Multi-Agent Systems
Christos K. Verginis, Zhe Xu, Ufuk Topcu
We develop a learning-based algorithm for the distributed formation control of networked multi-agent systems governed by unknown, nonlinear dynamics. Most existing algorithms eithe…
Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks
Nasim Baharisangari, Kazuma Hirota, Ruixuan Yan +2
Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis.…
Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach
Franck Djeumou, Zhe Xu, Murat Cubuktepe +1
We develop a probabilistic control algorithm, , for swarms of agents with heterogeneous dynamics and objectives, subject to high-level task specifications. The r…
Privacy-Utility Trade-Offs Against Limited Adversaries
Xiaoming Duan, Zhe Xu, Rui Yan +1
We study privacy-utility trade-offs where users share privacy-correlated useful information with a service provider to obtain some utility. The service provider is adversarial in t…
Non-Parametric Neuro-Adaptive Control Subject to Task Specifications
Christos K. Verginis, Zhe Xu, Ufuk Topcu
We develop a learning-based algorithm for the control of autonomous systems governed by unknown, nonlinear dynamics to satisfy user-specified spatio-temporal tasks expressed as sig…
Uncertainty-Aware Signal Temporal Logic Inference
Nasim Baharisangari, Jean-Raphaël Gaglione, Daniel Neider +2
Temporal logic inference is the process of extracting formal descriptions of system behaviors from data in the form of temporal logic formulas. The existing temporal logic inferenc…
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking
Aris Kanellopoulos, Filippos Fotiadis, Chuangchuang Sun +4
In this paper, we develop safe reinforcement-learning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specif…
Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach
Jean-Raphaël Gaglione, Daniel Neider, Rajarshi Roy +2
We address the problem of inferring descriptions of system behavior using Linear Temporal Logic (LTL) from a finite set of positive and negative examples. Most of the existing appr…
Controller Synthesis for Multi-Agent Systems with Intermittent Communication and Metric Temporal Logic Specifications
Zhe Xu, Federico M. Zegers, Bo Wu +3
This paper investigates the controller synthesis problem for a multi-agent system (MAS) with intermittent communication. We adopt a relay-explorer scheme, where a mobile relay agen…
Provably Correct Controller Synthesis of Switched Stochastic Systems with Metric Temporal Logic Specifications: A Case Study on Power Systems
Zhe Xu, Yichen Zhang
In this paper, we present a provably correct controller synthesis approach for switched stochastic control systems with metric temporal logic (MTL) specifications with provable pro…
Robust Pandemic Control Synthesis with Formal Specifications: A Case Study on COVID-19 Pandemic
Zhe Xu, Xiaoming Duan
Pandemics can bring a range of devastating consequences to public health and the world economy. Identifying the most effective control strategies has been the imperative task all a…
Control Strategies for COVID-19 Epidemic with Vaccination, Shield Immunity and Quarantine: A Metric Temporal Logic Approach
Zhe Xu, Bo Wu, Ufuk Topcu
Ever since the outbreak of the COVID-19 epidemic, various public health control strategies have been proposed and tested against the coronavirus SARS-CoV-2. We study three specific…
Constrained Active Classification Using Partially Observable Markov Decision Processes
Bo Wu, Niklas Lauffer, Mohamadreza Ahmadi +3
In this work, we study the problem of actively classifying the attributes of dynamical systems characterized as a finite set of Markov decision process (MDP) models. We are interes…
Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology
Bo Wu, Steven Carr, Suda Bharadwaj +2
We study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively. I…
Temporal Logic Inference for Hybrid System Observation with Spatial and Temporal Uncertainties
Zhe Xu, Yi Deng, Agung Julius
In this paper, we present a mechanism for building hybrid system observers to differentiate between specific positions of the hybrid system. The mechanism is designed through infer…
Adaptive Teaching of Temporal Logic Formulas to Learners with Preferences
Zhe Xu, Yuxin Chen, Ufuk Topcu
Machine teaching is an algorithmic framework for teaching a target hypothesis via a sequence of examples or demonstrations. We investigate machine teaching for temporal logic formu…