9 research outputs found
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
We consider the problem of learning sparse polymatrix games from observations
of strategic interactions. We show that a polynomial time method based on
-group regularized logistic regression recovers a game, whose Nash
equilibria are the -Nash equilibria of the game from which the data
was generated (true game), in samples of
strategy profiles --- where is the maximum number of pure strategies of a
player, is the number of players, and is the maximum degree of the game
graph. Under slightly more stringent separability conditions on the payoff
matrices of the true game, we show that our method learns a game with the exact
same Nash equilibria as the true game. We also show that
samples are necessary for any method to consistently recover a game, with the
same Nash-equilibria as the true game, from observations of strategic
interactions. We verify our theoretical results through simulation experiments
Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities
In this paper, we study the problem of learning the exact structure of
continuous-action games with non-parametric utility functions. We propose an
regularized method which encourages sparsity of the coefficients of
the Fourier transform of the recovered utilities. Our method works by accessing
very few Nash equilibria and their noisy utilities. Under certain technical
conditions, our method also recovers the exact structure of these utility
functions, and thus, the exact structure of the game. Furthermore, our method
only needs a logarithmic number of samples in terms of the number of players
and runs in polynomial time. We follow the primal-dual witness framework to
provide provable theoretical guarantees.Comment: arXiv admin note: text overlap with arXiv:1911.0422
Real-Time and Energy-Efficient Routing for Industrial Wireless Sensor-Actuator Networks
With the emergence of industrial standards such as WirelessHART, process industries are adopting Wireless Sensor-Actuator Networks (WSANs) that enable sensors and actuators to communicate through low-power wireless mesh networks. Industrial monitoring and control applications require real-time communication among sensors, controllers and actuators within end-to-end deadlines. Deadline misses may lead to production inefficiency, equipment destruction to irreparable financial and environmental impacts. Moreover, due to the large geographic area and harsh conditions of many industrial plants, it is labor-intensive or dan- gerous to change batteries of field devices. It is therefore important to achieve long network lifetime with battery-powered devices.
This dissertation tackles these challenges and make a series of contributions. (1) We present a new end-to-end delay analysis for feedback control loops whose transmissions are scheduled based on the Earliest Deadline First policy. (2) We propose a new real-time routing algorithm that increases the real-time capacity of WSANs by exploiting the insights of the delay analysis. (3) We develop an energy-efficient routing algorithm to improve the network lifetime while maintaining path diversity for reliable communication. (4) Finally, we design a distributed game-theoretic algorithm to allocate sensing applications with near-optimal quality of sensing
The Local and Global Price of Anarchy of Graphical Games
This paper initiates a study of connections between local and global properties of graphical games. Specifically, we introduce a concept of local price of anarchy that quantifies how well subsets of agents respond to their environments. We then show several methods of bounding the global price of anarchy of a game in terms of the local price of anarchy. All our bounds are essentially tight