Lillian J. Ratliff
ratliffl at eecs.berkeleyPh.D. Candidate, EECS at UC Berkeley
My research interests lie at the intersection of non-cooperative games, incentive design, statistical learning, and societal scale cyber-physical systems (S-CPS). We now collect very large and complex data sets through new sensing and control technologies that are currently being deployed in critical infrastructure such as healthcare and transportation systems as well as the smart grid. Owing to the scale of wireless sensor-actuator networks, existence of machine-to-machine interfaces operating in real-time, and large numbers of decision makers competing over oft–scarce resources, game theory and data-driven analytics do not alone suffice to capture the fundamental properties of intelligent infrastructures—game theory generally assumes knowledge of the environment and data-driven analytics cannot capture competitive interactions. However, both are necessary; game theory captures complex socioeconomic interactions while statistical learning aids in reducing dimensionality and provides tools for analysis of large-scale, time-series data. I am interested in developing data-driven models for system and agent behavior in S-CPS and utilizing these models to design incentives for behavior modification. Further, pervasive sensing and mis-aligned incentives give rise to new vulnerabilities such as privacy and security issues. Currently I am working on designing privacy-preserving and resilient incentive strategies for sustainability in S-CPS.Differential topology and game theory: (show/hide)
The aim of this project is to utilize tools from differential topology and geometry to study the interaction of competitive agents. In particular, by looking at games in which the strategy spaces are non-convex (either finite or infinite dimensional), we may develop useful decompositions in order to study existence of equilibria and their properties in this more abstract setting. Further, we have developed a characterization of local Nash equilibria—differential Nash equilibria—that is generic, structurally stable, and amenable to computation thereby allowing for the replacement of computationally intractable inequalities defining Nash equilibria in both the incentive design and utility learning problems with conditions that only need to be checked at a single point. The goal is to use the analytical and computational advancements to developing novel schemes for decentralized control in engineered systems as well as on–line identification techniques for human agents. (Collaborator: Samuel Burden [UC Berkeley])
We model the interaction between non-cooperative agents and a social planner as a reversed Stackelberg game where the social planner is the leader and the agents are the followers that play simultaneously. The social planner would like to modify the agents' behavior so that it aligns with some desired behavior which allows the social planner to meet her objective. However, we assume that the social planner does not know the agents' utility functions and hence must learn them in order to design incentives which modify the agents behavior. Using the differential Nash concept, we are able to cast the problem of solving for the parameters of the agents' utility functions and incentive mechanism as convex optimization problems making the method scalable. We enforce a second-order, non-degeneracy condition resulting in the leader-induced differential Nash equilibrium being isolated—hence, robust to measurement noise and modeling errors.
Through a social game we integrate building occupants into the control and management of an office building that is instrumented with networked embedded systems for sensing and actuation. The goal of the social game is to both incentivize building occupants to be more energy efficient and learn behavioral models for occupants so that the building can be made resilient and sustainable through automation. The social game for energy savings that we have designed is such that occupants in an office building vote according to their usage preferences of shared resources and are rewarded with points based on how energy efficient their strategy is in comparison with the other occupants. Having points increases the likelihood of the occupant winning in a lottery. We have employed tools from statistical learning theory to develop an algorithm for computing the stopping time given a specified level of accuracy, an underlying game-theoretic generative model for behavior, and a estimation or classification task, e.g. estimating the distribution of players' utility function parameters, classifying players into categories, forecasting energy usage—useful for demand response. (Collaborators: Ming Jin [UC Berkeley], Ioannis Konstantakopoulos [UC Berkeley], Costas Spanos [UC Berkeley])
The goal of this project is to design pricing mechanisms to coordinate non-cooperative agents. Applications that are considered include efficient energy management in buildings, controlled diffusion in multi-agent networks, and network security. In a non-cooperative game theoretic framework, the goal is to design pricing schemes to close the gap between the decentralized and the centralized cost. In addition, we are interested in designing robust pricing mechanisms. In an effort to do so, we first characterize when feedback Nash strategies are stable and use this characterization to inform the design of pricing mechanisms. (Collaborators: Sam Coogan [UC Berkeley], Daniel Calderone [UC Berkeley])
Disaggregation is the process of taking an aggregated signal and decomposing it into its components. The application we are interested in is energy disaggregation where the aggregate signal is the power consumption for a whole building (residential or commercial) and the goal is to decompose the aggregate signal into the power signal for all the contributing devices. By providing the disaggregated data to consumers, we are enabling them to construct methods for energy efficient operation of the building. Disaggregation of consumer energy data evokes privacy concerns. We study the inherent trade-off between privacy and the utility of the data. (Collaborators: Aaron Bestick [UC Berkeley], Henrik Ohlsson [C3 Energy, UC Berkeley], Roy Dong [UC Berkeley])
We are currently revisiting the microscopic to macroscopic view of multi-lane traffic with a game theoretic view of lane-changing by taking a physics-based, ground-up approach and employing tools from the theory of mean-field games to study the limit as the number of vehicles grows large. This game-theoretic model can be leveraged in the design of tolling and incentive schemes based on driver or lane type. (Collaborator: Dan Calderone [UC Berkeley])
CDC, LA, Dec. 2014, Organizing and speaking at workshop on 'Big Data Analytics for Societal Scale Cyber-Physical Systems: Energy Systems'
Allerton, IL, Oct. 2014, Social Game for Building Energy Efficiency: Incentive Design.
IFAC, Cape Town, South Africa, August 2014, Incentive Design and Utility Learning via Energy Disaggregation.
ACC, Portland Oregon, 06 June 2014, Genericty and Structural Stability of Non-degenerate Differential Nash Equilibria
Stanford, 30 May 2014, Game Theoretic Tools for Societal Scale Cyber-Physical Systems
PDE Seminar, Math Department Berkeley, 09 May 2014, Convexity in Multidimensional Screening
PDE Seminar, Math Department Berkeley, 25 April 2014, Multidimensional Screening
EECS Seminar, University of Michigan, 25 March 2014, Game Theoretic Tools for Societal Scale CPS
Lillian J. Ratliff, Ming Jin, Ioannis Konstantakopoulos, Costas Spanos, and S. Shankar Sastry. "Utility Learning and Incentive Design for Sustainability in Building Energy Management," 2015.
Lillian J. Ratliff, Pushkin Kachroo, and S. Shankar Sastry. "Observability and Resilience in Networked Transportation Systems." 2015.
Lillian J. Ratliff, Daniel Calderone, Samuel Coogan, S. Shankar Sastry. "Pricing for Coordination in Dynamic Games." 2015.
Lillian J. Ratliff, Samuel A. Burden, S. Shankar Sastry. "On the Characterization of Local Nash Equilibria in Continuous Games." IEEE Transactions on Automatic Control, 2014. Submitted (Nov.) (arXiv:1411.2168)
Lillian J. Ratliff, Carlos Barreto, Roy Dong, Henrik Ohlsson, Alvaro A. Cárdenas, and S. Shankar Sastry. Effects of Risk on Privacy Contracts for Demand-Side Management. IEEE Transactions on Smart Grid, 2014. Submitted (Sept.) (arXiv:1409.7926v3)
Pushkin Kachroo, Lillian J. Ratliff, and S. Shankar Sastry. "Analysis of the Godunov Based Hybrid Model for Ramp Metering and Robust Feedback Control Design." IEEE Transactions on Intelligent Transportation Systems, Vol. 15, Issue 5, 2132-2142, Oct. 2014. ( PDF)
Roy Dong, Alvaro A. Cárdenas, Lillian J. Ratliff, Henrik Ohlsson, Shankar Sastry. "Quantifying the Utility-Privacy Tradeoff in the Smart Grid." IEEE Transactions on Smart Grid, 2014. Under Review (May). ( arXiv:1406.2568v1)
Pushkin Kachroo, Lillian J. Ratliff, and S. Shankar Sastry. "A New Static Traffic Assignment Using Density Based Travel Time." Applied Mathematical Modeling, 2014. Under Review (July).
Ming Jin, Lillian J. Ratliff, Ioannis C. Konstantakopoulos, Costas Spanos, S. Shankar Sastry. "REST: A Reliable Estimation and Stopping Time Algorithm for Social Game Experiments." ICCPS, 2015. To Appear. PDF coming soon.
Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, Alvaro A. Cardenas, Shankar Sastry. "Privacy and Customer Segmentation in the Smart Grid." CDC, 2014. ( PDF)
Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, S. Shankar Sastry. "Incentive Design and Utility Learning via Energy Disaggregation." IFAC, 2014. (PDF)
Daniel J. Calderone, Lillian J. Ratliff, Shankar Sastry. "Pricing for Coordination in Open-Loop Differential Games." IFAC, 2014. (PDF)
Henrik Ohlsson, Lillian J. Ratliff, Roy Dong, S. Shankar Sastry. "Blind Identification via Lifting." IFAC, 2014. (PDF)
Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, Shankar Sastry. "Fundamental Limits of Non-Intrusive Load Monitoring." Conference on High Confidence Networked Systems (HiCoNS), 2014. DOI: 10.1145/2566468.2566471 (arXiv)
Lillian J. Ratliff, Samuel A. Burden, Shankar Sastry. "Genericity and Structural Stability of Non-degenerate Differential Nash Equilibria." American Control Conference, 2014. DOI: 10.1109/ACC.2014.6858848 (PDF)
Lillian J. Ratliff, Samuel A. Burden, Shankar Sastry. "Characterization and Computation of Local Nash Equilibria in Continuous Games." 51st Annual Allerton Conference on Communication, Control, and Computing, 2013. DOI: 10.1109/Allerton.2013.6736623 (PDF, Slides)
Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, S. Shankar Sastry. "Energy Disaggregation via Adaptive Filtering." 51st Annual Allerton Conference on Communication, Control, and Computing, 2013. DOI: 10.1109/Allerton.2013.6736521 (arXiv)
Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, Shankar Sastry. "A Dynamical Systems Approach to Energy Disaggregation." IEEE Conference on Decision and Control, 2013. pg. 6335-6340. DOI: 10.1109/CDC.2013.6760891 (arXiv, PDF)
Daniel Calderone, Lillian J. Ratliff, S. Shankar Sastry. "Pricing Design for Robustness in Linear-Quadratic Dynamic Games." IEEE Conference on Decision and Control, 2013. DOI: 10.1109/CDC.2013.6760558
Aaron Bestick, Lillian J. Ratliff, Po Yan, Ruzena Bajcsy, S. Shankar Sastry. "An Inverse Correlated Equilibrium Framework for Utility Learning in Multiplayer, Noncooperative Settings." Conference on High Confidence Networked Systems, 2013. DOI: 10.1145/2461446.2461449 (PDF)
Samuel Coogan, Lillian J. Ratliff, Daniel Calderone, Claire Tomlin, S. Shankar Sastry. "Energy Management via Pricing in LQ Dynamic Games." American Control Conference, 2013. DOI: 10.1109/ACC.2013.6579877 (PDF)
Lillian J. Ratliff, Samuel Coogan, Daniel Calderone, S. Shankar Sastry. " Pricing in Linear-Quadratic Dynamic Games." 50th Annual Allerton Conference on Communication, Control, and Computing, 2012. DOI: 10.1109/Allerton.2012.6483440 (PDF)
Lillian J. Ratliff and Pushkin Kachroo. "Validating numerically consistent macroscopic traffic models using microscopic data." Transportation Research Board 89th Annual Meeting, 2010.
Daniel P. Cook, Yitung Chen, Lillian J. Ratliff, Huajun Chen, and Jian Ma. "Numerical Modeling of EM Pump Efficiency." ASME Conference Proceedings, 2006.
Henrik Ohlsson, Lillian Ratliff, Roy Dong, Shankar Sastry. "Blind Identification of ARX Models with Piecewise Constant Inputs." arXiv:1303.6719, 2013. (PDF)
Lillian J. Ratliff, Samuel A. Burden, S. Shankar Sastry. "Characterization and Computation of Local Nash Equilibria in Continuous Games." at TRUST Annual Conference, Washington, D.C. April, 2013. (PDF)
Lillian Ratliff, Daniel Calderone, Samuel Coogan, S. Shankar Sastry. "Pricing for Coordination in Dynamic Games." at FORCES Kickoff Meeting, Washington, D.C. April, 2013. (PDF)
Daniel Calderone, Samuel Coogan, Lillian Ratliff, Anil Aswani, Claire Tomlin, S. Shankar Sastry. "Quadratic incentive design for noncooperative distributed control" Conference on High Confidence Networked Systems (HiCoNS) at CPS Week 2012, Beijing, China, May 2012. (PDF)
NSF Graduate Fellowship, 2009
MS thesis topic: Uncertainty Propagation in Dynamical Systems.
Advisor: Pushkin Kachroo