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The increasing deployment of AI is shaping the future landscape of the internet, which is set to become an integrated ecosystem of AI agents.
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Automated mechanism design: Complexity results stemming from the single-agent setting
Vincent Conitzer and Tuomas Sandholm · 2003
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Dov Monderer and Moshe Tennenholtz · 2003
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Self-interested automated mechanism design and implications for optimal combinatorial auctions
Vincent Conitzer and Tuomas Sandholm · 2004
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Haoqi Zhang, Yiling Chen, and David Parkes · 2008
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A kernel-based iterative combinatorial auction
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Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan · 2014
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Markov decision processes: discrete stochastic dynamic programming
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Payment rules through discriminant-based classifiers
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Human-level control through deep reinforcement learning
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Automated design of revenue-maximizing combinatorial auctions
Tuomas Sandholm and Anton Likhodedov · 2015
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan · 2016
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Automated mechanism design without money via machine learning
Harikrishna Narasimhan, Shivani Brinda Agarwal, and David C Parkes · 2016
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Cooperative multi-agent control using deep reinforcement learning
Jayesh K Gupta, Maxim Egorov, and Mykel Kochenderfer · 2017
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Multi-agent reinforcement learning in sequential social dilemmas
Joel Z Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, and Thore Graepel · 2017
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A converse to Banach’s fixed point theorem and its CLS-completeness
Constantinos Daskalakis, Christos Tzamos, and Manolis Zampetakis · 2018
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Learning with opponent-learning awareness
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Deep learning for multi-facility location mechanism design
Noah Golowich, Harikrishna Narasimhan, and David C Parkes · 2018
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Inequity aversion improves cooperation in intertemporal social dilemmas
Edward Hughes, Joel Z Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueñez-Guzman, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, et al · 2018
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Rllib: Abstractions for distributed reinforcement learning
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Value-decomposition networks for cooperative multi-agent learning based on team reward
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Optimal auctions through deep learning
Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David Parkes, and Sai Srivatsa Ravindranath · 2019
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Simple versus optimal contracts
Paul Dütting, Tim Roughgarden, and Inbal Talgam-Cohen · 2019
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COLA: Consistent learning with opponent-learning awareness
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Environment design for biased decision makers
G. Yu and C.-J. Ho · 2022
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Proximal learning with opponent-learning awareness
Stephen Zhao, Chris Lu, Roger Baker Grosse, and Jakob Nicolaus Foerster · 2022
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Parl: A unified framework for policy alignment in reinforcement learning
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A scalable neural network for DSIC affine maximizer auction design
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Tom Eccles, Edward Hughes, János Kramár, Steven Wheelwright, and Joel Z Leibo · 2019
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, DJ Strouse, Joel Z Leibo, and Nando De Freitas · 2019
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Learning fairness in multi-agent systems
Jiechuan Jiang and Zongqing Lu · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Evolving intrinsic motivations for altruistic behavior
Jane X Wang, Edward Hughes, Chrisantha Fernando, Wojciech M Czarnecki, Edgar A Duéñez-Guzmán, and Joel Z Leibo · 2019
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Adaptive mechanism design: Learning to promote cooperation
Tobias Baumann, Thore Graepel, and John Shawe-Taylor · 2020
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Zhijian Duan, Haoran Sun, Yurong Chen, and Xiaotie Deng · 2023
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Matthias Gerstgrasser and David C Parkes · 2023
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Mediated multi-agent reinforcement learning
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Similarity-based cooperative equilibrium
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Data market design through deep learning
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Delegated classification
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Nexusraven: a commercially-permissive language model for function calling
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Gymnasium, March 2023
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Deep contract design via discontinuous networks
Tonghan Wang, Paul Dütting, Dmitry Ivanov, Inbal Talgam-Cohen, and David C. Parkes · 2023
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Resolving social dilemmas with minimal reward transfer
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Steering no-regret learners to a desired equilibrium
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The sample complexity of online contract design
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Delegating data collection in decentralized machine learning
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