Research

The next generation urban ecosystem, increasingly empowered by the Internet of Things, has at its core a sharing economy where resources, both physical and information-based, are easily aggregated and exchanged via novel sharing mechanisms. As a result, a new market place is emerging in which these resources are a valuable commodity. However, the evolution of this market brings with it many challenges including 1) a large number of self-interested entities operating in the environment, 2) a rapidly growing number of sharing mechanisms, 3) a wide variety of resource constraints, and 4) new classes of vulnerabilities.

Our group works at the interface of computational intelligence and control, game/economic, and statistical learning theory to address these challenges.

Human decision-makers are integral components of societal-scale infrastructure systems, often impacting both supply and demand (e.g., in new sharing economy models such as ride-sharing or other labor markets or in the smart grid where people can now both produce and consume energy). Yet, their behavior does not jive with completely rational decision-making models. We are interested in develop techniques for integrating behavioral models that have been shown to capture human decision-making well with classical economic models and new learning algorithms. Moreover, we are working on developing certifiable algorithms for learning and designing control (both economic and physical) in closed loop.

Example applications include urban mobility where we are using massive real-world multi-modal route choice data to model dynamic, incomplete information decision-making, urban parking where we are modeling and designing policies to mitigate congestion via more efficient curbside parking utilization (collaboration with the Seattle Department of Transportation), and multi-sided data markets where we are modeling competition in emerging data markets.

Data Markets


Between Two Firms 

Urban Mobility


Multi-Modal 

Human-Machine Interaction


Human-Machine 

Currently our work focuses primarily on problems in societal scale systems (e.g., intelligent infrastructure systems). However, we seek algorithms and methodological innovations that have broad applicability. Many of the same kinds of uncertainties (particularly, those arising from human decision-makers) arise in a number of applications. For example, in (semi-)autonomous driving and robotic surgery applications humans are directly coupled with another autonomous agent and real-time decisions are being made often over short horizons where there is little time for cogitation and risk or reference points (e.g., past experiences) are the basis for decisions.

Applications Areas: Human-Machine Interaction & Autonomy, Data Markets, Societal-Scale CPS (Urban Mobility, Smart Parking, Smart Buildings)

Papers :

2017

 
Tanner Fiez, Lillian J. Ratliff, Chase Dowling, and Baosen Zhang. Data-Driven Spatio-Temporal Modeling of Parking Demand. In submitted to ACC, 2017. [ bib ]
 
Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff, and Baosen Zhang. Uncertainty in Multi-Commodity Routing Networks: When does it help? arxiv:1709.08441, 2017. [ bib ]
 
Shreyas Sekar, Lillian J. Ratliff, Tanner Fiez, and Liyuan Zheng. Combinatorial Bandit Approach to Matching Incentives. in preperation, 2017. [ bib ]
 
Lillian Ratliff, Shreyas Sekar, Tanner Fiez, and Liyuan Zheng. Regret Bounds for Bandits with Rewards Generated by a Single Markov Process. in preperation, 2017. [ bib ]
 
Lillian J. Ratliff and Eric Mazumdar. Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. arXiv:1703.09842, 2017. [ bib ]
 
Kamil Nar, Lillian J. Ratliff, and S. Shankar Sastry. Learning Prospect Theory Value Function and Reference Point of a Sequential Decision Maker. In Proceedings of the 56th IEEE Confefence on Decision and Control, 2017. [ bib ]
 
Eric Mazumdar, Lillian J. Ratliff, Tanner Fiez, and S. Shankar Sastry. Gradient--Based Inverse Risk-Sensitive Reinforcement Learning with Applications. In Proceedings of the 56th IEEE Confefence on Decision and Control, 2017. [ bib ]
 
Chase Dowling, Tanner Fiez, Lillian J. Ratliff, and Baosen Zhang. Optimizing Curbside Parking Resources Subject to Congestion Constraints. In Proceedings of the 56th IEEE Confefence on Decision and Control, 2017. [ bib ]
 
Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, and S. Shankar Sastry. Statistical Estimation in Competitive Settings with Strategic Data Sources. In Proceedings of the 56th IEEE Confefence on Decision and Control, 2017. [ bib ]
 
Chase Dowling, Tanner Fiez, Lillian J. Ratliff, and Baosen Zhang. How Much Urban Traffic is Searching for Parking? In arXiv:1702.06156, 2017. [ bib ]
 
Ioannis Konstantakopoulos*, Lillian J. Ratliff*, Ming Jin, and Costas Spanos. Leveraging Correlations in Utility Learning. In Proceedings of the American Control Conference, 2017. [ bib ]

2016

 
Roy Dong, Alvaro A. Cárdenas, Lillian J. Ratliff, Henrik Ohlsson, and Shankar Sastry. Quantifying the Utility-Privacy Tradeoff in the Smart Grid. ACM Transactions on Cyber-Physical Systems (under review; arXiv:1406.2568v1), 2016. [ bib ]
 
L. J. Ratliff, C. Dowling, E. Mazumdar, and B. Zhang. To observe or not to observe: Queuing game framework for urban parking. In Proceedings of the IEEE 55th Conference on Decision and Control (CDC), pages 5286--5291, Dec 2016. [ bib | DOI ]
 
Ioannis Konstantakopoulos*, Lillian J. Ratliff*, Ming Jin, S. Shankar Sastry, and Costas Spanos. A Robust Utility Learning Framework via Inverse Optimization. IEEE Transactions on Control Systems Technology (to appear), 2016. [ bib ]
 
D. Calderone, E. Mazumdar, L. J. Ratliff, and S. S. Sastry. Understanding the impact of parking on urban mobility via routing games on queue-flow networks. In Proceedings of the IEEE 55th Conference on Decision and Control, pages 7605--7610, Dec 2016. [ bib | DOI ]
 
Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, Costas Spanos, and S. Shankar Sastry. Smart building energy efficiency via social game: a robust utility learning framework for closing-the-loop. In Proceedings of the 1st International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE) in partnership with Global City Teams Challenge (GCTC) (SCOPE - GCTC), pages 1--6, April 2016. [ bib | DOI ]
 
I. C. Konstantakopoulos, L. J. Ratliff, M. Jin, C. J. Spanos, and S. S. Sastry. Inverse modeling of non-cooperative agents via mixture of utilities. In Proceedings of the IEEE 55th Conference on Decision and Control, pages 6327--6334, Dec 2016. [ bib | DOI ]
 
Lillan J. Ratliff, Samuel A. Burden, and S. Shankar Sastry. On the Characterization of Local Nash Equilibria in Continuous Games. IEEE Transactions on Automatic Control, 61(8):2301--2307, Aug. 2016. [ bib | DOI ]

2015

 
Incentivizing Efficiency in Societal-Scale Cyber-Physical Systems. Lillian J. Ratliff. PhD thesis, University of California, Berkeley, 2015. [ bib ]
 
D. Scobee, L. Ratliff, R. Dong, H. Ohlsson, M. Verhaegen, and S. S. Sastry. Nuclear norm minimization for blind subspace identification (N2BSID). In Proceedings of the 54th IEEE Conference on Decision and Control, pages 2127--2132, Dec 2015. [ bib | DOI ]
 
Ming Jin, Lillian J. Ratliff, Ioannis Konstantakopoulos, Costas Spanos, and Shankar Sastry. REST: A Reliable Estimation of Stopping Time Algorithm for Social Game Experiments. In Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, ICCPS '15, pages 90--99, New York, NY, USA, 2015. ACM. [ bib | DOI | http ]
 
D. J. Calderone, L. J. Ratliff, and S. S. Sastry. Lane pricing via decision-theoretic lane changing model of driver behavior. In Proceedings of the 54th IEEE Conference on Decision and Control, pages 3457--3462, Dec 2015. [ bib | DOI ]

2014

 
Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, and S. Shankar Sastry. Energy Efficiency via Incentive Design and Utility Learning. In Proceedings of the 3rd International Conference on High Confidence Networked Systems, HiCoNS '14, pages 57--58, New York, NY, USA, 2014. ACM. [ bib | DOI ]
 
Roy Dong, Lillian Ratliff, Henrik Ohlsson, and S. Shankar Sastry. Fundamental Limits of Nonintrusive Load Monitoring. In Proceedings of the 3rd International Conference on High Confidence Networked Systems, HiCoNS '14, pages 11--18, New York, NY, USA, 2014. ACM. [ bib | DOI ]
 
Henrik Ohlsson, Lillian Ratliff, Roy Dong, and S. Shankar Sastry. Blind Identification via Lifting. IFAC Proceedings Volumes, 47(3):10367 -- 10372, 2014. [ bib | DOI ]
 
Daniel Calderone, Lillian J. Ratliff, and S. Shankar Sastry. Pricing for Coordination in Open--Loop Differential Games. IFAC Proceedings Volumes, 47(3):9001 -- 9006, 2014. [ bib | DOI ]
 
Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, and S. Shankar Sastry. Incentive Design and Utility Learning via Energy Disaggregation. IFAC Proceedings Volumes, 47(3):3158 -- 3163, 2014. [ bib | DOI ]
 
L. J. Ratliff, R. Dong, H. Ohlsson, A. A. Cárdenas, and S. S. Sastry. Privacy and customer segmentation in the smart grid. In Proceedings of the 53rd IEEE Conference on Decision and Control, pages 2136--2141, Dec 2014. [ bib | DOI ]
 
L. J. Ratliff, S. A. Burden, and S. S. Sastry. Genericity and structural stability of non-degenerate differential Nash equilibria. In Proceedings of the American Control Conference, pages 3990--3995, June 2014. [ bib | DOI ]
 
L. J. Ratliff, M. Jin, I. C. Konstantakopoulos, C. Spanos, and S. S. Sastry. Social game for building energy efficiency: Incentive design. In Proceedings of the 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 1011--1018, Sept 2014. [ bib | DOI ]
 
Lillian J Ratliff, Roy Dong, Henrik Ohlsson, and S Shankar Sastry. Incentive Design and Utility Learning via Energy Disaggregation. In Proceedings of the 19th World Congress of the International Federation of Automatic Control, 2014. [ bib ]
 
Lillian Ratliff, Carlos Barreto, Roy Dong, Henrik Ohlsson, Alvaro A. Cardenas, and S. Shankar Sastry. Effects of Risk on Privacy Contracts for Demand-Side Management. arxiv:1409.7926v3, 2014. [ bib ]
 
P. Kachroo, L. Ratliff, and S. Sastry. Analysis of the Godunov-Based Hybrid Model for Ramp Metering and Robust Feedback Control Design. IEEE Transactions on Intelligent Transportation Systems, 15(5):2132--2142, Oct 2014. [ bib | DOI ]

2013

 
Aaron Bestick, Lillian J. Ratliff, Posu Yan, Ruzena Bajcsy, and S. Shankar Sastry. An Inverse Correlated Equilibrium Framework for Utility Learning in Multiplayer, Noncooperative Settings. In Proceedings of the 2Nd ACM International Conference on High Confidence Networked Systems, HiCoNS '13, pages 9--16, New York, NY, USA, 2013. ACM. [ bib | DOI ]
 
Henrik Ohlsson, Lillian Ratliff, Roy Dong, and S. Shankar Sastry. Blind Identification of ARX Models with Piecewise Constant Inputs. arxiv:1303.6719, 2013. [ bib ]
 
R. Dong, L. J. Ratliff, H. Ohlsson, and S. S. Sastry. Energy disaggregation via adaptive filtering. In Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 173--180, Oct 2013. [ bib | DOI ]
 
L. J. Ratliff, S. A. Burden, and S. S. Sastry. Characterization and computation of local Nash equilibria in continuous games. In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 917--924, Oct 2013. [ bib | DOI ]
 
D. J. Calderone, L. J. Ratliff, and S. S. Sastry. Pricing design for robustness in linear quadratic games. In 52nd IEEE Conference on Decision and Control, pages 4349--4354, Dec 2013. [ bib | DOI ]
 
L. J. Ratliff, R. Dong, H. Ohlsson, A. A. Cárdenas, and S. S. Sastry. Privacy and customer segmentation in the smart grid. In Proceedings of the 53rd IEEE Conference on Decision and Control, pages 2136--2141, Dec 2014. [ bib | DOI ]
 
S. Coogan, L. J. Ratliff, D. Calderone, C. Tomlin, and S. S. Sastry. Energy management via pricing in LQ dynamic games. In Proceedings of the American Control Conference, pages 443--448, June 2013. [ bib | DOI ]
 
R. Dong, L. Ratliff, H. Ohlsson, and S. S. Sastry. A dynamical systems approach to energy disaggregation. In Proceedings of the 52nd IEEE Conference on Decision and Control, pages 6335--6340, Dec 2013. [ bib | DOI ]

2012

 
L. J. Ratliff, S. Coogan, D. Calderone, and S. S. Sastry. Pricing in linear-quadratic dynamic games. In Proceedings of the 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 1798--1805, Oct 2012. [ bib | DOI ]

2010

 
Lillian J. Ratliff and Pushkin Kachroo. Validating numerically consistent macroscopic traffic models using microscopic data. Transportation Research Board Annual Meeting, 2010. [ bib ]

Funding

This work is funded by the National Science Foundation (CNS-1634136, CNS-1646912, CNS-1656873)

Awards

2017 National Science Foundation CISE Research Initiation Initiative Award
2009 National Science Foundation Graduate Research Fellowship