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Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.
La théorie du jeu et les équations intégrales à noyau symétrique
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Iterative Solutions of Games by Fictitious Play
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Nonlinear Multiobjective Optimization
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Coevolving predator and prey robots: Do “arms races” arise in artificial evolution?
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A Game-Theoretic Approach to the Simple Coevolutionary Algorithm
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A Monotonic Archive for Pareto-Coevolution
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Pareto Optimality in Coevolutionary Learning
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Planning in the presence of cost functions controlled by an adversary
McMahan, H. B., Gordon, G., and Blum, A · 2003
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Generalised weakened fictitious play
Leslie, D. and Collins, E. J · 2006
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Coevolution of neural networks using a layered Pareto archive
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The Colonel Blotto game
Roberson, B · 2006
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Methods for empirical game-theoretic analysis
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A New Algorithm for Generating Equilibria in Massive Zero-Sum Games
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Generative Adversarial Nets
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Fictitious Self-Play in Extensive-Form Games
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Defining and Simulating Open-Ended Novelty: Requirements, Guidelines, and Challenges
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Open-Ended Evolution: Perspectives from the OEE Workshop in York
Taylor, T., Bedau, M., Channon, A., Ackley, D., Banzhaf, W., Beslon, G., Dolson, E., Froese, T., Hickinbotham, S., Ikegami, T., McMullin, B., Packard, N., Rasmussen, S., Virgo, N., Agmon, E., McGregor, E. C. S., Ofria, C., Ropella, G., Spector, L., Stanley, K. O., Stanton, A., Timperley, C., Vostinar, A., and Wiser, M · 2016
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Minimal Criterion Coevolution: A New Approach to Open-Ended Search
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Hansen, T. D., Miltersen, P. B., and Sørensen, T. B · 2008
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Exploiting Open-Endedness to Solve Problems Through the Search for Novelty
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Coevolution of Fitness Predictors
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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
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Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castaneda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., Sonnerat, N., Green, T., Deason, L., Leibo, J. Z., Silver, D., Hassabis, D., Kavukcuoglu, K., and Graepel, T · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., and Hassabis, D · 2018
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Wang, R., Lehman, J., Clune, J., and Stanley, K. O · 2019
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