Resilient Control of Cyber-Physical Systems with Distributed Learning - July 2020
PI(s) and Co-PI(s): Sayan Mitra and Geir Dullerud and Sanjay Shakkotai (U. Texas at Austin)
Researchers: Dawei Sun and Negin Musavi
HARD PROBLEM(S) ADDRESSED
This refers to Hard Problems, released November 2012.
Resiliency: Effective verification of safety and security properties of autonomous and cyber-physical systems
Metrics: How much data is necessary to achieve a certain level of confidence regarding a safety/security claim
PUBLICATIONS
Papers written as a result of your research from the current quarter only.
Working paper:
Optimistic Optimization for Statistical Model Checking with Regret Bounds, Musavi, Sun, Mitra, Shakkottai, and Dullerud, April 2020. Available online from https://arxiv.org/abs/1911.01537
HOOVER tool available from: https://github.com/sundw2014/HooVer
Related recent publications:
Verifying Cyber-Physical Systems: A Path to Safe Autonomy, Sayan Mitra, to be published by MIT Press, 2020-21.
Warm Starting Bandits with Side Information from Confounded Data, N. Sharma, S. Basu, K. Shanmugam and S. Shakkottai, arXiv 2002.08405, 2020. Available at: https://arxiv.org/abs/2002.08405
Differential Privacy for Sequential Algorithms, Yu Wang, Hussein Sibai, Sayan Mitra, and Geir Dullerud, submitted.
Verifying PCTL Specifications on Markov Decision Processes via Reinforcement Learning, Yu Wang, Nima Roohi, Matthew West, Mahesh Viswanathan and Geir Dullerud, submitted.
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems, Joao Porto, Bin Hu, and Geir Dullerud, Proceedings of the American Control Conference, 2020.
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems, Joao Porto, Bin Hu, and Geir Dullerud, Proceedings of the Learning for Dynamics and Control Workshop, 2020.
The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits, Ronshee Chawla, Abishek Sankararaman, Ayalvadi J. Ganesh and Sanjay Shakkottai, Proceedings of AISTATS 2020, Palermo, Sicily, June 2020.
Data-driven safety verification of complex cyber-physical systems, Chuchu Fan and Sayan Mitra. A chapter in the book titled Design Automation for Cyber-Physical Systems, edited by Mohammad Abdullah Al Faruquqe and Arquimedes Canedo, pages 107-143, Springer, 2019.
Using symmetry transformations in equivariant dynamical systems for their safety verification Hussein Sibai, Navid Mokhlesi and Sayan Mitra; accepted for publication in the proceedings of the Seventeenth International Symposium on Automated Technology for Verification and Analysis (ATVA), October 28-31, 2019, Taipei City, Taiwan. Nominated for best paper award
KEY HIGHLIGHTS
Each effort should submit one or two specific highlights. Each item should include a paragraph or two along with a citation if available. Write as if for the general reader of IEEE S&P.
The purpose of the highlights is to give our immediate sponsors a body of evidence that the funding they are providing (in the framework of the SoS lablet model) is delivering results that "more than justify" the investment they are making.
Two PhD students are dedicating their research time to the project. We have formulated a new direction of scientific enquiry into safety and security analysis of systems. The approach relies on distributed and sample-efficient optimization techniques that have been developed in the context of the Multi-armed bandit problem. We have shown how these optimization algorithms can be used effectively for statistical model checking of markov decision processes. We have built a suite of benchmarks related to online safety analysis of autonomous and semi-autonomous vehicles. Our initial results are very promising as the data usage and the running time of our algorithms can be several orders of magnitude better than existing model checking approaches such as Storm and Prism. The prototype tool has been made available online.
COMMUNITY ENGAGEMENTS
We organized and hosted the SOS Quaterly meeting for Fall 2019 in Chicago, November 5-6th, Discovery Partners Institute https://cps-vo.org/SoSLmtg/UIUC/2019
Optimal data rate estimation and model detection for safe autonomy. USC-MHI Cyber-Physical Systems Seminar promoted by the Center for Cyber-Physical Systems and the Internet-of-Things (CCI), lecture given by Sayan Mitra, October 16th, 2019.
Mitra gave an invited lecture at the 10th Anniversary of the Distinguished Colloquium Series of University of Maryland (sponsored by Booz, Allen, and Hamilton) on Verification for safe autonomy: Challenges and recent developments, May 3rd, 2019.
Hyper-parameter Tuning for ML Models: A Monte-Carlo Tree Search (MCTS) Approach. USC-MHI Cyber-Physical Systems Seminar promoted by the Center for Cyber-Physical Systems and the Internet-of-Things (CCI), lecture given by Sanjay Shakkottai, October 2th, 2019.
Hyper-parameter Tuning for ML Models: A Monte-Carlo Tree Search (MCTS) Approach. ECE CSP Seminar, University of Michigan at Ann Arbor, lecture given by Sanjay Shakkottai, October 24, 2019.
Statistical Validation and Principle-Based Simulation of Complex Cyber-Controlled Systems, CES Symposium Lecture, University of Texas, Austin, lecture given by Geir Dullerud, December 3rd, 2019.
Hyper-parameter Tuning for ML Models: A Monte-Carlo Tree Search (MCTS) Approach. EE Seminar, IIT Bombay, lecture given by Sanjay Shakkottai, December 17, 2019.
Hyper-parameter Tuning for ML Models: A Monte-Carlo Tree Search (MCTS) Approach. EE Seminar, Indian Institute of Science, lecture given by Sanjay Shakkottai, January 2, 2020.
On the Throughput vs Accuracy Trade-Off for Streaming Unsupervised Classification, Sanjay Shakkottai, Workshop on Learning Theory 2, Tata Institute of Fundamental Research, lecture given by Sanjay Shakkottai, January 3, 2020.
Learning and Resource Allocation in Networks: Finite Time Bounds and Insights, Invited speaker at the Symposium on Advances in Communication Networks, Indian Institute of Science, Bangalore, July 10, 2020.
Learning and Resource Allocation in Networks: Finite Time Bounds and Insights, Keynote talk, The 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt 2020), June 17, 2020.
Pursuing high-impact activities: Advise from distinguished scientists in the NeTS community, Invited panelist at the NSF NeTS Community CAREER Webinar, lecture given by Sanjay Shakkottai, June 17, 2020.
Warm starting adaptive interventions with side information from confounded logs, invited talk, University of Arizona at Tuscon Applied Math COVID-19 Working Group, May 12, 2020.
Warm starting adaptive interventions with side information from confounded logs, Invited panelist and panel moderator, The First NSF NeTS Community Workshop Call to Arms Workshop on modeling, analysis and mitigation of COVID-19, lecture given by Sanjay Shakkottai, April 13, 2020.
Learning and Statistical Validation of Complex Cyber-PhysicalSystems, Plenary Speaker, Formal Methods in Mathematics Workshop, Mathematics, Carnegie Mellon University, lecture given by Geir Dullerud, January 7th, 2020 on logic in engineering systems.
Statistical Validation and Principle-Based Simulation of Complex Cyber-Controlled Systems, online plenary lecture, Forum on Robotics & Control(FoRCE), lecture by Geir Dullerud, scheduled for September 4th, 2020.
EDUCATIONAL ADVANCES
The second edition of PI Mitra's new course Principles of Safe Autonomy at University of Illinois is coming to a successful conclusion this semester with a larger class size (38 students) despite the setbacks from the COVID19 outbreak. The course takes a deep dive into the seminal topics in object recognition, localization, decision making, path planning, and safety verification. Graduate and undergraduate students from ECE and CS are completing the course. The course team has designed 6 New programming assignments involving topics such as lane detection, road-sign recognition, localization with particle filters, decision making with reinforcement learning, path planning with rapidly expanding random trees, and safety verification using simulation-driven proofs. With support from the Illinois Center for Autonomy, we have setup a laboratory with 7 workstations with GPUs for performing simulation-based experiments. The students are using ROS, Gazebo, for testing their programming assignments. Find out more about the safe autonomy course and the student projects at https://publish.illinois.edu/safe-autonomy/