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Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts.
Model-based reinforcement learning for atari
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Human-timescale adaptation in an open-ended task space
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Xxii. programming a computer for playing chess
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Human problem solving
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The mind’s eye in chess
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Mental models: Towards a cognitive science of language, inference, and consciousness
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Intelligence without representation
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Why the child’s theory of mind really is a theory
Gopnik, A. and Wellman, H. M. (1992) · 1992
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The scientist as child
Gopnik, A. (1996) · 1996
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Planning and acting in partially observable stochastic domains
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Deep blue
Campbell, M., Hoane Jr, A. J., and Hsu, F.-h. (2002) · 2002
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Theory-based bayesian models of inductive learning and reasoning
Tenenbaum, J. B., Griffiths, T. L., and Kemp, C. (2006) · 2006
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Core knowledge
Spelke, E. S. and Kinzler, K. D. (2007) · 2007
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The structure and dynamics of scientific theories: A hierarchical bayesian perspective
Henderson, L., Goodman, N. D., Tenenbaum, J. B., and Woodward, J. F. (2010) · 2010
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Concepts and folk theories
Gelman, S. A. and Legare, C. H. (2011) · 2011
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How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., and Goodman, N. D. (2011) · 2011
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Reconstructing constructivism: causal models, bayesian learning mechanisms, and the theory theory
Gopnik, A. and Wellman, H. M. (2012) · 2012
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The origins of inquiry: Inductive inference and exploration in early childhood
Schulz, L. (2012) · 2012
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M. (2013) · 2013
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A video game description language for model-based or interactive learning
Schaul, T. (2013) · 2013
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Invention as a combinatorial process: evidence from us patents
Youn, H., Strumsky, D., Bettencourt, L. M., and Lobo, J. (2015) · 2015
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Quality diversity: A new frontier for evolutionary computation
Pugh, J. K., Soros, L. B., and Stanley, K. O. (2016) · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · 2016
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Deep reinforcement learning with double q-learning
Van Hasselt, H., Guez, A., and Silver, D. (2016) · 2016
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Quality and diversity optimization: A unifying modular framework
Cully, A. and Demiris, Y. (2017) · 2017
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Intuitive theories
Gerstenberg, T. and Tenenbaum, J. B. (2017) · 2017
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2017) · 2017
Scientific discovery in the age of artificial intelligence
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · 2023
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Using games to understand the mind
Allen, K., Brändle, F., Botvinick, M., Fan, J. E., Gershman, S. J., Gopnik, A., Griffiths, T. L., Hartshorne, J. K., Hauser, T. U., Ho, M. K., et al. (2024) · 2024
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Language models, world models, and human model-building
Andreas, J. (2024) · 2024
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Building machines that learn and think with people
Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi-Xuan, T., Ho, M., Mansinghka, V., et al. (2024) · 2024
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Goals as reward-producing programs
Davidson, G., Todd, G., Togelius, J., Gureckis, T. M., and Lake, B. M. (2024) · 2024
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Investigating human priors for playing video games
Dubey, R., Agrawal, P., Pathak, D., Griffiths, T. L., and Efros, A. A. (2018) · 2018
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Ha, D. and Schmidhuber, J. (2018) · 2018
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Multi-goal reinforcement learning: Challenging robotics environments and request for research
Plappert, M., Andrychowicz, M., Ray, A., McGrew, B., Baker, B., Powell, G., Schneider, J., Tobin, J., Chociej, M., Welinder, P., et al. (2018) · 2018
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Artificial intelligence and games
Yannakakis, G. N. and Togelius, J. (2018) · 2018
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General video game ai: A multitrack framework for evaluating agents, games, and content generation algorithms
Perez-Liebana, D., Liu, J., Khalifa, A., Gaina, R. D., Togelius, J., and Lucas, S. M. (2019) · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · 2019
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Understanding world or predicting future? a comprehensive survey of world models
Ding, J., Zhang, Y., Shang, Y., Zhang, Y., Zong, Z., Feng, J., Yuan, Y., Su, H., Li, N., Sukiennik, N., et al. (2024) · 2024
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Violations of core object principles change adults’ behaviors in maze games
Liu, R. and Xu, F. (2024) · 2024
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Gavel: Generating games via evolution and language models
Todd, G., Padula, A. G., Stephenson, M., Piette, É., Soemers, D. J., and Togelius, J. (2024) · 2024
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Evaluating the world model implicit in a generative model
Vafa, K., Chen, J., Rambachan, A., Kleinberg, J., and Mullainathan, S. (2024) · 2024
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The rise and fall of technological development in virtual communities
Vélez, N., Wu, C. M., Gershman, S. J., and Schulz, E. (2024) · 2024
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People use fast, goal-directed simulation to reason about novel games
Zhang, C. E., Collins, K. M., Wong, L., Barba, M., Weller, A., and Tenenbaum, J. B. (2024) · 2024
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A rational model of innovation by recombination
Zhao, B., Vélez, N., and Griffiths, T. (2024) · 2024
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Is sora a world simulator? a comprehensive survey on general world models and beyond
Zhu, Z., Wang, X., Zhao, W., Min, C., Deng, N., Dou, M., Wang, Y., Shi, B., Wang, K., Zhang, C., et al. (2024) · 2024
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Arc-agi-3
ARC Prize (2025) · 2025
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Autumnbench: World model learning in humans and ai
Basis (2025) · 2025
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Superintelligent agents pose catastrophic risks: Can scientist ai offer a safer path?
Bengio, Y., Cohen, M., Fornasiere, D., Ghosn, J., Greiner, P., MacDermott, M., Mindermann, S., Oberman, A., Richardson, J., Richardson, O., et al. (2025) · 2025
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Arc-agi-2: A new challenge for frontier ai reasoning systems
Chollet, F., Knoop, M., Kamradt, G., Landers, B., and Pinkard, H. (2025) · 2025
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Generation and evaluation in the human invention process through the lens of game design
Collins, K. M., Todd, G., Wong, L., Zhang, C., Togelius, J., Weller, A., Chu, J., Griffiths, T., and Tenenbaum, J. (2025) · 2025
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Are large language models reliable ai scientists? assessing reverse-engineering of black-box systems
Geng, J., Chen, H., Arumugam, D., and Griffiths, T. L. (2025) · 2025
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Robin: A multi-agent system for automating scientific discovery
Ghareeb, A. E., Chang, B., Mitchener, L., Yiu, A., Szostkiewicz, C. J., Laurent, J. M., Razzak, M. T., White, A. D., Hinks, M. M., and Rodriques, S. G. (2025) · 2025
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Guertler, L., Cheng, B., Yu, S., Liu, B., Choshen, L., and Tan, C. (2025) · 2025
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General agents need world models
Richens, J., Abel, D., Bellot, A., and Everitt, T. (2025) · 2025
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Measuring general intelligence with generated games
Verma, V., Huang, D., Chen, W., Klein, D., and Tomlin, N. (2025) · 2025
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Leveraging procedural generation to benchmark reinforcement learning
Cobbe, K., Hesse, C., Hilton, J., and Schulman, J. (2020) · 2056
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