Structure learning for approximate solution of many-player games

Z Li and MP Wellman 34th AAAI Conference on Artificial Intelligence, pages 2119-2127, Feb 2020. Abstract Games with many players are difficult to solve or even specify without adopting structural assumptions that enable representation in…
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Peter Wurman inducted into Nat’l Inventors Hall of Fame

SRG alum Pete Wurman (PhD '99) is part of a distinguished group inducted in the Inventors Hall of Fame this year. See announcement here.

Iterated Deep Reinforcement Learning in Games: History-Aware Training for Improved Stability

M Wright, Y Wang, and MP Wellman Proceedings of the 20th ACM Conference on Economics and Computation, pages 617-636, June 2019. Abstract Deep reinforcement learning (RL) is a powerful method for generating policies in complex environments,…

Deception in Finitely Repeated Security Games

TH Nguyen, Y Wang, A Sinha, and MP Wellman 33rd AAAI Conference on Artificial Intelligence, Jan/Feb 2019. Abstract Allocating resources to defend targets from attack is often complicated by uncertainty about the attacker’s capabilities,…

A Learning and Masking Approach to Secure Learning

L Nguyen, S Wang, and A Sinha Ninth Conference on Decision and Game Theory for Security, October 2018. Abstract Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed…

Bounding regret in empirical games

S Jecmen, A Sinha, Z Li, L Tran-Thanh 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. Abstract Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack…