Robust Stackelberg Equilibria in Extensive-Form Games and Extension to Limited Lookahead
November 21, 2017 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Christian Kroer, Gabriele Farina, Tuomas Sandholm
arXiv ID
1711.08080
Category
cs.GT: Game Theory
Cross-listed
cs.AI,
cs.MA
Citations
25
Venue
AAAI Conference on Artificial Intelligence
Last Checked
6 months ago
Abstract
Stackelberg equilibria have become increasingly important as a solution concept in computational game theory, largely inspired by practical problems such as security settings. In practice, however, there is typically uncertainty regarding the model about the opponent. This paper is, to our knowledge, the first to investigate Stackelberg equilibria under uncertainty in extensive-form games, one of the broadest classes of game. We introduce robust Stackelberg equilibria, where the uncertainty is about the opponent's payoffs, as well as ones where the opponent has limited lookahead and the uncertainty is about the opponent's node evaluation function. We develop a new mixed-integer program for the deterministic limited-lookahead setting. We then extend the program to the robust setting for Stackelberg equilibrium under unlimited and under limited lookahead by the opponent. We show that for the specific case of interval uncertainty about the opponent's payoffs (or about the opponent's node evaluations in the case of limited lookahead), robust Stackelberg equilibria can be computed with a mixed-integer program that is of the same asymptotic size as that for the deterministic setting.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Game Theory
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid
R.I.P.
π»
Ghosted
Computing Resource Allocation in Three-Tier IoT Fog Networks: a Joint Optimization Approach Combining Stackelberg Game and Matching
R.I.P.
π»
Ghosted
Fast Convergence of Regularized Learning in Games
R.I.P.
π»
Ghosted
Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks
R.I.P.
π»
Ghosted
Blockchain Mining Games
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted