Empirical game-theoretic methods for adaptive cyber-defense

MP Wellman, TH Nguyen, and M Wright in S Jajodia et al. (Eds.): Adversarial and Uncertain Reasoning for Adaptive Cyber Defense, LNCS 11830, pages 112–128, 2019. Abstract Game-theoretic applications in cyber-security are often restricted…

Market manipulation: An adversarial learning framework for detection and evasion

X Wang and MP Wellman 29th International Joint Conference on Artificial Intelligence, Special Track on AI in FinTech, pages 4626–4632, 2020. Abstract We propose an adversarial learning framework to capture the evolving game between a regulator…

Generating realistic stock market order streams

J Li, X Wang, Y Lin, A Sinha, and MP Wellman 34th AAAI Conference on Artificial Intelligence, pages 727-734, Feb 2020. Abstract We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial…

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…

Cap-and-trade emissions regulation: A strategic analysis

F Cheng , Y Engel, and MP Wellman Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 187–193, August 2019. Abstract Cap-and-trade schemes are designed to achieve target levels of regulated emissions…

Probably almost-stable strategy profiles in simulation-based games

M Wright and MP Wellman AAMAS-19 Workshop on Games, Agents and Incentives, May 2019. Abstract Empirical studies of strategic settings commonly model player interactions under supposed game-theoretic equilibrium behavior, to predict what…

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,…
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My interview with Bill Powers on AI Decision Makers

https://youtu.be/SnTf-iWUTpk Recorded June 2018, as part of a series on Machine Behavior, in conjunction with publication of a position paper in Nature on the topic.