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In recent years there has been a tremendous surge in the general capabilities of AI systems, mainly fuelled by training foundation models on internetscale data.
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Antifragile: Things that gain from disorder , volume 3
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A comprehensive survey on safe reinforcement learning
J. Garcıa and F. Fernández · 2015
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Illuminating search spaces by mapping elites
J.-B. Mouret and J. Clune · 2015
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Why Greatness Cannot Be Planned: The Myth of the Objective
K. Stanley and J. Lehman · 2015
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Requirements for open-ended evolution in natural and artificial systems
T. Taylor · 2015
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Concrete problems in ai safety
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Unifying count-based exploration and intrinsic motivation
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Faulty reward functions in the wild
J. Clark and D. Amodei · 2016
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On statistical learning via the lens of compression
O. David, S. Moran, and A. Yehudayoff · 2016
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Cooperative inverse reinforcement learning
D. Hadfield-Menell, S. J. Russell, P. Abbeel, and A. D. Dragan · 2016
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Quality diversity: A new frontier for evolutionary computation
J. K. Pugh, L. B. Soros, and K. O. Stanley · 2016
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Mastering the game of Go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
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The role of subjectivity in the evaluation of open-endedness
K. O. Stanley and L. Soros · 2016
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The flash crash: A new deconstruction
E. M. Aldrich, J. Grundfest, and G. Laughlin · 2017
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Minimal criterion coevolution: a new approach to open-ended search
J. C. Brant and K. O. Stanley · 2017
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Potential surprise theory as a theoretical foundation for scenario planning
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Curiosity-driven exploration by self-supervised prediction
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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, Dec. 2017
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Open-endedness: The last grand challenge you’ve never heard of
K. O. Stanley, J. Lehman, and L. Soros · 2017
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Exploration by Random Network Distillation, Oct. 2018
Y. Burda, H. Edwards, A. Storkey, and O. Klimov · 2018
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AI safety via debate, Oct. 2018
G. Irving, P. Christiano, and D. Amodei · 2018
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Illuminating generalization in deep reinforcement learning through procedural level generation
N. Justesen, R. R. Torrado, P. Bontrager, A. Khalifa, J. Togelius, and S. Risi · 2018
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Penalizing side effects using stepwise relative reachability
V. Krakovna, L. Orseau, R. Kumar, M. Martic, and S. Legg · 2018
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Routes to open-endedness in evolutionary systems
T. Taylor · 2018
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The modes toolbox: Measurements of open-ended dynamics in evolving systems
E. L. Dolson, A. E. Vostinar, M. J. Wiser, and C. Ofria · 2019
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Vision-language models as a source of rewards
K. Baumli, S. Baveja, F. Behbahani, H. Chan, G. Comanici, S. Flennerhag, M. Gazeau, K. Holsheimer, D. Horgan, M. Laskin, et al · 2023
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Language models can explain neurons in language models
S. Bills, N. Cammarata, D. Mossing, H. Tillman, L. Gao, G. Goh, I. Sutskever, J. Leike, J. Wu, and W. Saunders · 2023
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Quality-Diversity through AI Feedback, Oct. 2023
H. Bradley, A. Dai, H. Teufel, J. Zhang, K. Oostermeijer, M. Bellagente, J. Clune, K. Stanley, G. Schott, and J. Lehman · 2023
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Compression, generalization and learning
M. C. Campi and S. Garatti · 2023
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Human control: Definitions and algorithms
R. Carey and T. Everitt · 2023
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Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research, Mar. 2019
J. Z. Leibo, E. Hughes, M. Lanctot, and T. Graepel · 2019
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Adapting behaviour for learning progress
T. Schaul, D. Borsa, D. Ding, D. Szepesvari, G. Ostrovski, W. Dabney, and S. Osindero · 2019
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Model-based active exploration
P. Shyam, W. Jaskowski, and F. Gomez · 2019
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Why open-endedness matters
K. O. Stanley · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, J. Oh, D. Horgan, M. Kroiss, I. Danihelka, A. Huang, L. Sifre, T. Cai, J. P. Agapiou, M. Jaderberg, A. S. Vezhnevets, R. Leblond, T. Pohlen, V. Dalibard, D. Budden, Y. Sulsky, J. Molloy, T. L. Paine, C. Gulcehre, Z. Wang, T. Pfaff, Y. Wu, R. Ring, D. Yogatama, D. Wünsch, K. McKinney, O. Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, and D. Silver · 2019
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Emergent tool use from multi-agent autocurricula
B. Baker, I. Kanitscheider, T. M. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch · 2020
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AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence, Jan. 2020
J. Clune · 2020
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Vision-language models as a source of rewards
H. Chan, V. Mnih, F. Behbahani, M. Laskin, L. Wang, F. Pardo, M. Gazeau, H. Sahni, D. Horgan, K. Baumli, Y. Schroecker, S. Spencer, R. Steigerwald, J. Quan, G. Comanici, S. Flennerhag, A. Neitz, L. M. Zhang, T. Schaul, S. Singh, C. Lyle, T. Rocktäschel, J. Parker-Holder, and K. Holsheimer · 2023
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Language Modeling Is Compression, Sept. 2023
G. Delétang, A. Ruoss, P.-A. Duquenne, E. Catt, T. Genewein, C. Mattern, J. Grau-Moya, L. K. Wenliang, M. Aitchison, L. Orseau, M. Hutter, and J. Veness · 2023
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What can ai learn from human exploration? intrinsically-motivated humans and agents in open-world exploration
Y. Du, E. Kosoy, A. Dayan, M. Rufova, P. Abbeel, and A. Gopnik · 2023
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Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution, Sept. 2023
C. Fernando, D. Banarse, H. Michalewski, S. Osindero, and T. Rocktäschel · 2023
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Strategic Reasoning with Language Models, May 2023
K. Gandhi, D. Sadigh, and N. D. Goodman · 2023
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Critic: Large language models can self-correct with tool-interactive critiquing
Z. Gou, Z. Shao, Y. Gong, Y. Shen, Y. Yang, N. Duan, and W. Chen · 2023
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Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers, Sept. 2023
Q. Guo, R. Wang, J. Guo, B. Li, K. Song, X. Tan, G. Liu, J. Bian, and Y. Yang · 2023
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Language Models Represent Space and Time, Oct. 2023
W. Gurnee and M. Tegmark · 2023
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Exploration via Elliptical Episodic Bonuses, Jan. 2023
M. Henaff, R. Raileanu, M. Jiang, and T. Rocktäschel · 2023
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GAIA-1: A Generative World Model for Autonomous Driving, Sept. 2023
A. Hu, L. Russell, H. Yeo, Z. Murez, G. Fedoseev, A. Kendall, J. Shotton, and G. Corrado · 2023
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What went wrong? closing the sim-to-real gap via differentiable causal discovery
P. Huang, X. Zhang, Z. Cao, S. Liu, M. Xu, W. Ding, J. Francis, B. Chen, and D. Zhao · 2023
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Motif: Intrinsic Motivation from Artificial Intelligence Feedback, Sept. 2023
M. Klissarov, P. D’Oro, S. Sodhani, R. Raileanu, P.-L. Bacon, P. Vincent, A. Zhang, and M. Henaff · 2023
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STEVE-1: A Generative Model for Text-to-Behavior in Minecraft, June 2023
S. Lifshitz, K. Paster, H. Chan, J. Ba, and S. McIlraith · 2023
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Eureka: Human-Level Reward Design via Coding Large Language Models, Oct. 2023
Y. J. Ma, W. Liang, G. Wang, D.-A. Huang, O. Bastani, D. Jayaraman, Y. Zhu, L. Fan, and A. Anandkumar · 2023
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Language Model Crossover: Variation through Few-Shot Prompting, Feb. 2023
E. Meyerson, M. J. Nelson, H. Bradley, A. Moradi, A. K. Hoover, and J. Lehman · 2023
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Large Language Models as General Pattern Machines, July 2023
S. Mirchandani, F. Xia, P. Florence, B. Ichter, D. Driess, M. G. Arenas, K. Rao, D. Sadigh, and A. Zeng · 2023
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Levels of AGI: Operationalizing Progress on the Path to AGI, Nov. 2023
M. R. Morris, J. Sohl-dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, and S. Legg · 2023
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Understanding the capabilities of large language models for automated planning
V. Pallagani, B. Muppasani, K. Murugesan, F. Rossi, B. Srivastava, L. Horesh, F. Fabiano, and A. Loreggia · 2023
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Generative Agents: Interactive Simulacra of Human Behavior, Apr. 2023
J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
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Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty
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