- Workshop Dates: 1pm - 6pm on April 25 (Saturday), and 8am - noon on April 26 (Sunday)
- Deadline for abstract submission : March 31, 2015
- Notification of Acceptance: First Week of April
- Registration: April 10, 2015
Tamer Başar (Univ. of Illinois at Urbana-Champaign)
Stochastic Dynamic Teams with Asymmetric Information
In any real application of stochastic decision making, be it in the cooperative team framework or the non-cooperative game setting, asymmetry in the information acquired by different decision makers (synonymously agents or players) naturally arises. Presence of asymmetric information, particularly in dynamic (multi-stage) decision problems, creates challenges in the establishment of existence of optimal solutions (in teams) and non-cooperative equilibria (in games) as well as in their characterization and computation. No unified theory exists (such as dynamic programming or maximum principle) that would be applicable to such problems. In this talk, I will discuss our efforts toward developing such a unified theory with regard to the existence of solutions in stochastic dynamic teams. The framework will encompass problems with non-classical information, such as Witsenhausen’s 1968 counterexample (and its multi-dimensional extensions) and the Gaussian test channel (and its multi-relay versions with real-time information processing and transmission), among others, for which I will present a unified theory for the existence of team-optimal solutions. The approach first lifts the analysis to the space of behavioral strategies, establishing existence in that richer space, and then brings the solution down to the original team problem while respecting the informational relationships. Several examples will be provided to illustrate the solution technique, the underlying caveats, and the conditions involved. Some open problems and future directions for research will be identified, particularly with regard to dynamic stochastic games with asymmetric information.
(This is based on joint work with Abhishek Gupta, Serdar Yüksel, and Cedric Langbort.)
Tamer Başar has been with the University of Illinois at Urbana-Champaign since 1981, where he holds the academic positions of Swanlund Endowed Chair; Center for Advanced Study Professor of Electrical and Computer Engineering; Professor, Coordinated Science Laboratory; Professor, Information Trust Institute; and Affiliate Professor, Mechanical Sciences and Engineering. He is also the Director of the Center for Advanced Study. He is a member of the US National Academy of Engineering and the European Academy of Sciences; Fellow of IEEE, IFAC, and SIAM; a past president of the IEEE Control Systems Society (CSS), the founding president of the International Society of Dynamic Games (ISDG), and a past president of the American Automatic Control Council (AACC). He has received several awards and recognitions over the years, including the highest awards of IEEE CSS, IFAC, AACC, and ISDG, the IEEE Control Systems Technical Field Award, and a number of international honorary doctorates and professorships. Dr. Başar has over 650 publications in systems, control, communications, optimization, and dynamic games, including books on non-cooperative dynamic game theory, robust control, network security, wireless and communication networks, and stochastic networks. He is editor of several book series.
(Iowa State University)
Distributed computing under uncertainty
In this talk, we take the unifying view of systems interacting over communication networks as distributed computing systems and propose to study them as networked control systems. We first consider a popular algorithm for distributed averaging and show that it can generate a collective global complex behavior when the inter-agent communication happens over unreliable links. To mitigate the effects of the unreliable information exchange, we propose a new distributed averaging algorithm resilient to noise and intermittent communication. The algorithm and the control perspective are the basis for the development of new distributed optimization systems that we can analyze and design as networked control systems. We apply our approach to obtain new distributed least squares solvers and distributed solvers of system of linear equations, which are resilient to communication uncertainties.
Nicola Elia is a Professor of Electrical and Computer Engineering at Iowa State University. He received the Laurea degree in Electrical Engineering from Politecnico di Torino in 1987, and the Ph.D. degree in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 1996. He worked at the Fiat Research Center from 1987 to 1990. He was Postdoctoral Associate at the Laboratory for Information and Decision Systems at MIT from 1996 to 1999. He has received the NSF CAREER Award in 2001. His research interests include networked control systems, communication systems with access to feedback, complex systems, distributed optimization and control.
(Univ. of Illinois at Urbana-Champaign)
Characterization and Optimization of l∞ Gains of Linear Switched Systems
In this talk we consider l∞ gain characterizations of linear switched systems (LSS) and present various relevant results on their exact computation and optimization. Depending on the role of the switching sequence, we study two cases: first, when the switching sequence attempts to maximize, and second, when it attempts to minimize the l∞ gain. The first, named as worst-case, can be related to robustness of the system to uncontrolled switching; the second relates to situations when the switching can be part of the overall decision making.
Although, in general, the exact computation of l∞ gains is difficult, we provide specific classes, the input-output switching systems, for which it is shown that linear programming can be used to obtain the worst-case l∞ gain. This is a sufficiently rich class of systems as any stable LSS can be approximated by one. Certain applications to robust and optimal control design are provided as well as a new necessary and sufficient condition to check the stability of LSS in form of a model matching problem.
On the other hand, if one is interested in minimizing the l∞ gain over the switching sequences, we show that, for finite impulse response (FIR) switching systems the minimizing switching sequence can be chosen to be periodic. For input-only or output-only switching an exact, readily computable, characterization of the minimal gain is provided, and it is shown that the minimizing switching sequence is constant, something that is not true for input output switching.
Petros G. Voulgaris received the Diploma in Mechanical
Engineering from the National Technical
University, Athens, Greece, in 1986, and the S.M.
and Ph.D. degrees in Aeronautics and Astronautics
from the Massachusetts Institute of Technology,
Cambridge, in 1988 and 1991, respectively. Since
August 1991, he has been with the Department
of Aerospace Engineering, University of Illinois at
Urbana Champaign, where he is currently a Professor.
He also holds joint appointments with the
Coordinated Science Laboratory, and the department
of Electrical and Computer Engineering at the same university. His research
interests include robust and optimal control and estimation, communications
and control, networks and control, and applications of advanced control
methods to engineering practice including flight control, nano-scale control,
robotics, and structural control systems. Dr. Voulgaris is a recipient of the
National Science Foundation Research Initiation Award (1993), the Office of
Naval Research Young Investigator Award (1995) and the UIUC Xerox Award
for research. He has been an Associate Editor for the IEEE Transactions on
Automatic Control and the ASME Journal of Dynamic Systems, Measurement
and Control. He is also a Fellow of IEEE..
Mihailo Jovanovic (Univ. of Minnesota)
Sparsity-promoting optimal control of distributed systems
This talk is about design of feedback gains that achieve a desired tradeoff
between quadratic performance of distributed systems and controller
sparsity. Our approach consists of two steps. First, we identify sparsity
patterns of the feedback gains by incorporating sparsity-promoting penalty
functions into the optimal control problem, where the added terms penalize
the number of communication links in the distributed controller. Second, we
optimize feedback gains subject to structural constraints determined by the
identified sparsity patterns. In the first step, the sparsity structure of
feedback gains is identified using the alternating direction method of
multipliers, an algorithm well-suited to large optimization problems. This
method alternates between promoting the sparsity of the controller and
optimizing the closed-loop performance, which allows us to exploit the
structure of the corresponding objective functions. In particular, we take
advantage of the separability of the sparsity-promoting penalty functions
to decompose the minimization problem into sub-problems that can be solved
analytically. Even though the standard quadratic performance index is in
general a nonconvex function of the feedback gain, we identify classes of
convex problems that arise in the design of sparse undirected networks and
optimal sensor/actuator selection. In this case, the corresponding
synthesis problem can be formulated as a semidefinite program, implying
that the globally optimal sparse controller can be computed efficiently.
Several examples are provided to demonstrate the effectiveness of the
developed approach and the accompanying software LQRSP (available at
Mihailo R. Jovanovic (www.umn.edu/~mihailo) is an Associate Professor of
Electrical and Computer Engineering at the University of Minnesota. He has
held visiting positions with Stanford University and the Institute for
Mathematics and its Applications. His current research focuses on
fundamental limitations in the design of large dynamic networks,
sparsity-promoting optimal control, and dynamics and control of fluid
flows. He is a senior member of IEEE and currently serves as an Associate
Editor of the SIAM Journal on Control and Optimization. He served as an
Associate Editor of the IEEE Control Systems Society Conference Editorial
Board from July 2006 until December 2010. Prof. Jovanovic received a CAREER
Award from the National Science Foundation in 2007, an Early Career Award
from the University of Minnesota Initiative for Renewable Energy and the
Environment in 2010, a Resident Fellowship within the Institute on the
Environment at the University of Minnesota in 2012, the George S. Axelby
Outstanding Paper Award from the IEEE Control Systems Society in 2013, the
University of Minnesota Informatics Institute Transdisciplinary Research
Fellowship in 2014, and the Distinguished Alumni Award from UC Santa
Barbara in 2014.
(Iowa State University)
Model-Based Testing and Monitoring for Embedded Software
In many application domains, Simulink/Stateflow serves as a platform for model-based development of the reactive embedded code, that interacts with its environment in real-time fashion. The talk will present a model-based approach for testing Simulink/Stateflow code, based on its automated translation to input-output extended finite automaton (I/O-EFA), followed by automated test-generation, guaranteeing user-defined code as well as requirements coverage, and also support for automated test-execution and error-localization. While testing is useful for design-time error analysis, the talk will further discuss our model-based approach for run-time error monitoring, detection and localization. Monitoring at system level (as opposed to software level) is necessarily stochastic, and a more general I/O-Stochastic Hybrid Automaton (I/O-SHA) model is used, and condition is obtained for bounded-delay detectability, and achieving desired levels of false-positives/-negatives.
Ratnesh Kumar is a Professor of Electrical & Computer Engineering at the Iowa State University, and prior to which he was with the ECE Dept. at the Univ. of Kentucky. He received B.Tech. in Electrical Eng. from Indian Institute of Technology, Kanpur (IITK) in 1987, and M.S. and Ph.D. in Electrical & Computer Engineering from the Univ. of Texas, Austin (UTAustin) in 1989 and 1991, respectively. Ratnesh's research interest spans sensors, networks, controls and software with application domains of cyberphysical (hybrid) systems, embedded and real-time systems, model-based software and web-services, power systems, energy harvesting, and sustainable agriculture. Ratnesh received Gold Medals from IITK, MCD Fellowship and Dissertation Award from UTAustin, Fellowships from NASA-Ames, Applied Research Lab-Penn State Univ, Idaho National Lab, and United Technologies Research Center, and several awards from NSF (including Research Initiation Award), DoE, ONR, General Motors, and Adobe. Ratnesh is a Fellow of the IEEE for contributions to discrete event system modeling, control, diagnosis and applications . Ratnesh has served on a number of editorial boards for IEEE and ACM, has been General/Program Chair and also given keynote talks at IEEE and ACM conferences.
Vasu Salapaka (Univ. of Illinois at Urbana-Champaign)
Dynamic Coverage and Graph Aggregation: An Entropy Based Approach
This talk will consider seemingly unrelated problems of dynamic coverage and graph aggregation. Dynamic coverage problems are related to determining clusters in an ensemble of moving objects. These problems have received considerable attention lately mainly propelled by recent advances in geographic information systems, geopositioning and wireless sensor networks, which require deployment of mobile resources that continuously cover a set of mobile sites in a region. Graph aggregation problems refer to obtaining smaller graphs that are representative of large weighted directed graphs. Both physical modeling and data-based modeling methods typically yield large models with numerous nodes and complex interactions represented by edges; this makes the analysis of fundamental system behavior intractable. Therefore, to identify the dominant or ensemble interactions of a system, it is often necessary to have a simple representative graph that reflects the core structures. The Markov chain aggregation problem is an important special case of the graph simplification problem, which by itself represents a large class of applications areas. Even though these two problems have different and unrelated goals, a striking similarity among them is that after disregarding the details, they aim to solve similar resource allocation optimization problems. We will present a framework based on the Maximum Entropy Principle that gives a common viewpoint to these dissimilar problems. This framework inherits the key features of Determinstic Annealing (DA) algorithm, which was developed in the data compression/information theory literature for static vector quantization problems.
This talk will present algorithms and related computational issues for both these problems. In context of dynamic coverage problems, the proposed framework addresses both the coverage as well as tracking aspects of a dynamically evolving datasets. This framework allows us to address the inherent trade-off between the resolution of the clusters and the computation cost, and provides flexibility to a variety of dynamic specifications. For graph aggregation, an algorithm to simplify large weighted directed graphs by aggregating nodes and edges will be presented. In context of Markov chains, we show that these data driven aggregation algorithms preserve some dynamic properties, extending some seminal results in this area. We show that stationary distributions of aggregated chain well approximate the aggregated stationary distributions of the original chains.
Srinivasa M. Salapaka received the B.Tech. degree in Mechanical Engineering from Indian Institute of Technology in 1995, the M.S. and the Ph.D. degrees in Mechanical Engineering from the University of California at Santa Barbara, U.S.A in 1997 and 2002, respectively. During 2002-2004, he was a postdoctoral associate in the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, USA. Since January 2004, he has been a Faculty Member in Mechanical science and Engineering at the University of Illinois, Urbana-Champaign. He got the NSF CAREER award in the year 2005. His areas of current research interest include controls for nanotechnology, combinatorial optimization, Brownian ratchets, numerical/dynamic-systems analysis of root solving algorithms, and control of power systems.
Shreyas Sundaram (Purdue)
The Strategic Formation of Multi-Layer Networks
Many real-world networks consist of multiple layers of relationships between a common set of nodes. Examples include friendship and professional relationships in social networks, different transportation networks between a set of cities, and coupled communication and energy infrastructure networks. In this talk, we describe a model to capture the strategic formation of multi-layer networks, where each layer is designed to maximize some utility that depends on the topology of that layer and those of the other layers. We start by generalizing distance-based network formation to the two-layer setting, where edges are constructed in one layer (with fixed cost per edge) to minimize distances between nodes that are neighbors in another layer. We show that designing an optimal network in this setting is NP-hard. Despite the underlying complexity of the problem, we characterize certain properties of the optimal networks. We then formulate a multi-layer network formation game where each layer corresponds to a player that is optimally choosing its edge set in response to the edge sets of the other players. For utility functions that view the different layers as strategic substitutes, we show that players with low edge-costs drive players with high edge-costs out of the game, and that hub-and-spoke networks that are commonly observed in transportation systems arise as Nash equilibria in this game.
Shreyas Sundaram is an Assistant Professor in the School of Electrical and Computer Engineering at Purdue University. He received his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2009, and was a Postdoctoral Researcher at the University of Pennsylvania from 2009 to 2010. He was an Assistant Professor at the University of Waterloo from 2010 to 2014. At Waterloo, he received the Department of Electrical and Computer Engineering Research Award in 2014, the University of Waterloo Outstanding Performance Award in 2013 and the Faculty of Engineering Distinguished Performance Award in 2012. He received the M. E. Van Valkenburg Graduate Research Award and the Robert T. Chien Memorial Award from the University of Illinois, both for excellence in graduate research, and he was a finalist for the Best Student Paper Award at the 2007 and 2008 American Control Conferences. His research interests include network science, large-scale dynamical systems, fault-tolerant and secure control, linear system and estimation theory, and the application of algebraic graph theory to system analysis.
(Iowa State University)
The Enabling Role of Utility Theory, Game Theory and Mechanism Design in a new Value-based System Engineering Approach for Large-scale Complex System Design
Large-Scale Complex Engineered Systems (LSCES) have characteristics that result in unique and significant challenges during the system design and development process. The costs and risks for developing these systems are extremely large. Hundreds, thousands, or even tens of thousands of individuals are involved, across multiple organizations. Often, the system being developed cannot be fully understood, verified or tested until completion, at which time, performance issues may not be able to be adequately or easily addressed in a timely or cost-effective manner. Failure often means that companies go under, projects are cancelled, and decision-makers lose their jobs, with national or even international repercussions. The increasing complexity of these systems has grown over the past fifty years to a completely unmanageable mess, with losses in DoD topping $200M per day due to cost and time overruns and project cancellations. The complexity has grown primarily from the increased number of couplings arising from disciplines, components, organizations, and geographically-distributed individuals. This explosion in couplings, of very disparate natures, has led to unintended consequences arising from unknown or unexpected interactions, particularly in light of the decomposition process presently in place that propagates requirements down to lower levels.
Recent workshops, special conference sessions, and presentations have repeatedly identified the need to better integrate social and technical issues during the design of LSCES, by drawing more heavily from disciplines traditionally outside the scope of engineering. There is now a recognized need for a transformation in engineering practice that embraces and exploits the interactions of decision makers and the technologies used in the design process. In the research presented here, we identify new approaches to better enable decision-making consistency across levels and stages in the design process through the inclusion of utility theory, game theory and mechanism design.
Professor Christina Bloebaum joined the faculty of Aerospace Engineering at Iowa State University as the Dennis and Rebecca Muilenburg Professor of Aerospace Engineering in August 2012. Prior to that time, she was the Program Director for the Engineering and Systems Design (ESD), System Science (SYS), Design of Engineering Material Systems (DEMS) and EFRI-ODISSEI programs at the National Science Foundation between 2009-2012. She was on leave from the University at Buffalo, where she had been a member of the faculty, holding numerous administrative and research positions, since 1991. Professor Bloebaum's present research area is in the design of large-scale complex engineered systems. She has spent her career looking at challenges in the Multidisciplinary Design Optimization (MDO) field, developing new optimization, visualization and tradespace methodologies for these complex systems. She has most recently been developing approaches in which decisions on how the complex product or system will be used is incorporated in the simulation through Decision Analysis, Game Theory and Mechanism Design. Professor Bloebaum is the 2012 recipient of the American Institute of Aeronautics and Astronautics (AIAA) Multidisciplinary Design Optimization Award. She is a Fellow of the AIAA. She was the recipient of the SUNY Chancellor’s Award for Excellence in Teaching, was honored for Notable Contributions to Teaching and Learning at UB, was recognized by the SUNY Research Foundation for Excellence in Research, and was named a Visionary Innovator by UB’s office of technology transfer. She was the recipient of the first UB Chair for Competitive Product and Process Design while establishing the New York State Center for Engineering Design and Industrial Innovation (NYSCEDII), for which she was the Executive Director. Professor Bloebaum was recipient of the prestigious NSF Presidential Faculty Fellow Award. Professor Bloebaum has graduated 13 Ph.D. students, over 70 M.S. students, and has had over $7 Million in research funding.
(Air Force Research Lab)
A differential game of active target defense and missile guidance
The active target defense differential game considers an Attacker missile pursuing a Target aircraft. The aircraft is however aided by a Defender missile whose goal is to intercept the Attacker before it reaches the Target aircraft. Thus, a team is formed by the Target and the Defender which cooperate to maximize the separation between the Target aircraft and the point where the Attacker missile is intercepted by the Defender missile, while the Attacker simultaneously tries to minimize said distance. The solution to this differential game provides optimal heading angles for the Target and the Defender team to maximize the terminal separation between Target and Attacker at the instant of interception of the Attacker by the Defender, and it also provides the optimal heading angle for the Attacker to minimize this final separation.
Eloy Garcia is a research scientist with the Control Science Center of Excellence of the Air Force Research Laboratory, Wright-Patterson AFB, OH.
Dr. Garcia received the M.S. degree in Electrical Engineering from the University of Illinois at Chicago and the Ph. D. degree in Electrical Engineering from the University of Notre Dame. He has been at AFRL since 2012 where his research work focuses on the areas of cooperative missile guidance and cooperative control of multi-agent systems with limited communication.
(Univ. of Minnesota)
Reconstruction of interconnectedness in networks of dynamical systems based on passive observations
Determining interrelatedness structure of various entities from multiple time series data is of significant interest to many areas. Knowledge of such a structure can aid in identifying cause and effect relationships, clustering of similar entities, identification of representative elements and model reduction. In this talk, a methodology for identifying the interrelatedness structure of dynamically related time series data based on passive observations structure will be presented. The framework will allow for the presence of loops in the connectivity structure of the network. The quality of the reconstruction will be quantified. Results on the how the sparsity of multivariate Wiener filter, the Granger filter and the causal Wiener filter depend on the network structure will be presented. Connections to graphical models with notions of independence posed by d-separation will be highlighted.
Murti V. Salapaka was born in Andhra Pradesh, India, in 1969. He received the B.Tech. degree in Mechanical Engineering from the Indian Institute of Technology, Madras. He received the M.S. an Ph.D. degrees in Mechanical engineering from the University of California, Santa Barbara, in 1991, 1993, and 1997, respectively. From 1997-2007, he was with the Electrical Engineering Department at Iowa State University, From 2007 to 2010, he iwas an Associate Professor at University of Minnesota, Minneapolis, where he currently holds the Vincentine Hermes-Luh Chair in Electrical Engineering. Dr. Salapaka was the recipient of the 1997 National Science Foundation CAREER Award, and the 2001 Iowa State University Young Engineering Faculty Research Award. His research interests are in control and systems theory, nanotechnology and molecular biology.