Information Structure Design in Team Decision Problems

We consider a problem of information structure design in team decision problems and team games. We propose simple, scalable greedy algorithms for adding a set of extra information links to optimize team performance and resilience to non-cooperative and adversarial agents. We show via a simple counterexample that the set function mapping additional information links to team performance is in general not supermodular. Although this implies that the greedy algorithm is not accompanied by worst-case performance guarantees, we illustrate through numerical experiments that it can produce effective and often optimal or near optimal information structure modifications

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page

Perspectives in modern control theory

Bibliography: leaves 33-36.Prepared under ONR Contract N00014-76-C-0346.by Michael Athans

Game Development Framework Based Upon Sensors and Actuators

Urge for comfort and excitement have made gadgets indispensable part of our life. The technology-enabled gadgets not only facilitate and enrich our daily lives but also are interesting tools to challenge human imagination to design and implement new ubiquitous applications. Pervasive gaming, in which human interaction and game/scenario-dependent designs are often common practices, has proved to be one of the areas to successfully combine technology and the human fantasy. By moving away from games being played by humans and by focusing instead on games played by robots and giving humans the leading role of defining game strategies and players’ roles, this paper aims at bridging the two fields of robotics and wireless sensor/actuator networks and exploring their potentials in the field of pervasive gaming. A generic game development framework is introduced that accommodates different types of robots and various kinds of sensors and actuators. Being extensible and modular, the proposed framework can be used for a wide range of pervasive applications built upon sensors and actuators. To enable game development, a Wiimote-based robot identification and localization technique is presented. The proposed framework and robot identification, localization, control and communication mechanisms are evaluated by implementing a game example

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202