https:\/\/arxiv.org\/pdf\/2412.17799 <\/p>\n
References<\/p>\n
Wendy Aguilar, Guillermo Santamar\u00b4\u0131a-Bonfil, Tom Froese, and Carlos Gershenson. The past, present, and future of artificial life. Frontiers in Robotics and AI, 1:8, 2014.<\/p>\n
Jyrki Alakuijala, James Evans, Ben Laurie, Alexander Mordvintsev, Eyvind Niklasson, Ettore Randazzo,
\nLuca Versari, et al. Computational life: How well-formed, self-replicating programs emerge from simpleinteraction. arXiv preprint arXiv:2406.19108, 2024.<\/p>\n
Philip W Anderson. More is different: Broken symmetry and the nature of the hierarchical structure of science. Science, 177(4047):393\u2013396, 1972.<\/p>\n
Mark A Bedau, John S McCaskill, Norman H Packard, Steen Rasmussen, Chris Adami, David G Green,
\nTakashi Ikegami, Kunihiko Kaneko, and Thomas S Ray. Open problems in artificial life. Artificial life, 6 (4):363\u2013376, 2000.<\/p>\n
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S
\nBernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.<\/p>\n
Bert Wang-Chak Chan. Lenia-biology of artificial life. arXiv preprint arXiv:1812.05433, 2018.<\/p>\n
Bert Wang-Chak Chan. Lenia and expanded universe. In Artificial Life Conference Proceedings 32, pages
\n221\u2013229. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info . . . , 2020.<\/p>\n
Bert Wang-Chak Chan. Towards large-scale simulations of open-ended evolution in continuous cellular
\nautomata. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation,
\npages 127\u2013130, 2023.<\/p>\n
Jeff Clune. Ai-gas: Ai-generating algorithms, an alternate paradigm for producing general artificial intelligence. arXiv preprint arXiv:1905.10985, 2019.<\/p>\n
Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and
\nShirley Ho. Discovering symbolic models from deep learning with inductive biases. Advances in neural
\ninformation processing systems, 33:17429\u201317442, 2020.<\/p>\n
Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, and Edward Raff. Vqgan-clip: Open domain image generation and editing with natural language guidance. In European Conference on Computer Vision, pages 88\u2013105. Springer, 2022.<\/p>\n
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and
\nSergey Levine. Emergent complexity and zero-shot transfer via unsupervised environment design. Advances in neural information processing systems, 33:13049\u201313061, 2020.<\/p>\n
Maxence Faldor and Antoine Cully. Toward artificial open-ended evolution within lenia using quality-
\ndiversity. arXiv preprint arXiv:2406.04235, 2024.<\/p>\n
Maxence Faldor, Jenny Zhang, Antoine Cully, and Jeff Clune. Omni-epic: Open-endedness via models of
\nhuman notions of interestingness with environments programmed in code. arXiv preprint arXiv:2405.15568, 2024.<\/p>\n
Martin J Falk, Finnegan D Roach, William Gilpin, and Arvind Murugan. Curiosity-driven search for novel nonequilibrium behaviors. Physical Review Research, 6(3):033052, 2024.<\/p>\n
Kevin Frans, Lisa Soros, and Olaf Witkowski. Clipdraw: Exploring text-to-drawing synthesis through
\nlanguage-image encoders. Advances in Neural Information Processing Systems, 35:5207\u20135218, 2022.<\/p>\n
Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning.
\narXiv preprint arXiv:2210.02410, 2022.<\/p>\n
Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola.
\nDreamsim: Learning new dimensions of human visual similarity using synthetic data. arXiv preprint
\narXiv:2306.09344, 2023.<\/p>\n
Mathematical Games. The fantastic combinations of john conway\u2019s new solitaire game \u201clife\u201d by martin
\ngardner. Scientific American, 223:120\u2013123, 1970.<\/p>\n
David Ha and Yujin Tang. Collective intelligence for deep learning: A survey of recent developments.
\nCollective Intelligence, 1(1):26339137221114874, 2022.<\/p>\n
Vincent Herrmann, Louis Kirsch, and J\u00a8urgen Schmidhuber. Learning one abstract bit at a time through
\nself-invented experiments encoded as neural networks. arXiv preprint arXiv:2212.14374, 2022.<\/p>\n
Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal Behbahani, Aditi Mavalankar, Yuge Shi, Tom
\nSchaul, and Tim Rocktaschel. Open-endedness is essential for artificial superhuman intelligence. arXiv
\npreprint arXiv:2406.04268, 2024.<\/p>\n
Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. The platonic representation hypothesis.
\narXiv preprint arXiv:2405.07987, 2024.<\/p>\n
Minqi Jiang, Tim Rockt\u00a8aschel, and Edward Grefenstette. General intelligence requires rethinking exploration. Royal Society Open Science, 10(6):230539, 2023.<\/p>\n
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Z\u00b4\u0131dek, Anna Potapenko, et al. Highly accurate protein structure prediction with alphafold. nature, 596(7873):583\u2013589, 2021.<\/p>\n
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray,
\nAlec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint
\narXiv:2001.08361, 2020.<\/p>\n
Jacek Karwowski, Oliver Hayman, Xingjian Bai, Klaus Kiendlhofer, Charlie Griffin, and Joar Skalse. Goodhart\u2019s law in reinforcement learning. arXiv preprint arXiv:2310.09144, 2023.<\/p>\n
Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. Diffusionclip: Text-guided diffusion models for ro-
\nbust image manipulation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern
\nrecognition, pages 2426\u20132435, 2022.<\/p>\n
Andrei N Kolmogorov. On tables of random numbers. Theoretical Computer Science, 207(2):387\u2013395, 1998. Christopher Langton. Artificial life. 1992.<\/p>\n
Joel Lehman and Kenneth O Stanley. Abandoning objectives: Evolution through the search for novelty
\nalone. Evolutionary computation, 19(2):189\u2013223, 2011a.<\/p>\n
Joel Lehman and Kenneth O Stanley. Evolving a diversity of virtual creatures through novelty search and local competition. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pages 211\u2013218, 2011b.<\/p>\n
Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O Stanley. Evolution
\nthrough large models. In Handbook of Evolutionary Machine Learning, pages 331\u2013366. Springer, 2023.<\/p>\n
Seth Lloyd. Measures of complexity: a nonexhaustive list. IEEE Control Systems Magazine, 21(4):7\u20138, 2001.<\/p>\n
Chris Lu, Michael Beukman, Michael Matthews, and Jakob Foerster. Jaxlife: An open-ended agentic simulator. In ALIFE 2024: Proceedings of the 2024 Artificial Life Conference. MIT Press, 2024a.<\/p>\n
Chris Lu, Samuel Holt, Claudio Fanconi, Alex J Chan, Jakob Foerster, Mihaela van der Schaar, and
\nRobert Tjarko Lange. Discovering preference optimization algorithms with and for large language models.
\narXiv preprint arXiv:2406.08414, 2024b.<\/p>\n
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist:
\nTowards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292, 2024c.<\/p>\n
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke
\nZhu, Linxi Fan, and Anima Anandkumar. Eureka: Human-level reward design via coding large language
\nmodels. arXiv preprint arXiv:2310.12931, 2023.<\/p>\n
Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and projection
\nfor dimension reduction. arXiv preprint arXiv:1802.03426, 2018.<\/p>\n
Melanie Mitchell. Complexity: A guided tour. Oxford University Press, 2009.<\/p>\n
Melanie Mitchell, Peter Hraber, and James P Crutchfield. Revisiting the edge of chaos: Evolving cellular automata to perform computations. arXiv preprint adap-org\/9303003, 1993.<\/p>\n
Tom Mohr. How particle life emerges from simplicity. https:\/\/www.youtube.com\/watch?v=p4YirERTVF0 Accessed: 2024-09-30.<\/p>\n
Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson, and Michael Levin. Growing neural cellular
\nautomata. Distill, 5(2):e23, 2020.<\/p>\n
Alexander Mordvintsev, Eyvind Niklasson, and Ettore Randazzo. Particle lenia and the energy-based formulation. 2022 Cited on, page 49, 2022.<\/p>\n
Francisco Mota, Scott Aaronson, Lu\u00b4\u0131s Antunes, and Andr\u00b4e Souto. Sophistication as randomness deficiency.
\nIn Descriptional Complexity of Formal Systems: 15th International Workshop, DCFS 2013, London, ON,
\nCanada, July 22-25, 2013. Proceedings 15, pages 172\u2013181. Springer, 2013.<\/p>\n
Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites. arXiv preprint
\narXiv:1504.04909, 2015.<\/p>\n
Eleni Nisioti, Claire Glanois, Elias Najarro, Andrew Dai, Elliot Meyerson, Joachim Winther Pedersen,
\nLaetitia Teodorescu, Conor F Hayes, Shyam Sudhakaran, and Sebastian Risi. From text to life: On
\nthe reciprocal relationship between artificial life and large language models. In Artificial Life Conference Proceedings 36, volume 2024, page 39. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info . . . , 2024.<\/p>\n
Charles Ofria and Claus O Wilke. Avida: A software platform for research in computational evolutionary biology. Artificial life, 10(2):191\u2013229, 2004.<\/p>\n
Maxime Oquab, Timoth\u00b4ee Darcet, Th\u00b4eo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre
\nFernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual
\nfeatures without supervision. arXiv preprint arXiv:2304.07193, 2023.<\/p>\n
Norman H Packard. Adaptation toward the edge of chaos. Dynamic patterns in complex systems, 212:
\n293\u2013301, 1988.<\/p>\n
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefen-
\nstette, and Tim Rockt\u00a8aschel. Evolving curricula with regret-based environment design. In International Conference on Machine Learning, pages 17473\u201317498. PMLR, 2022.<\/p>\n
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning, pages 2778\u20132787. PMLR, 2017.<\/p>\n
Erwan Plantec, Gautier Hamon, Mayalen Etcheverry, Pierre-Yves Oudeyer, Cl\u00b4ement Moulin-Frier, and Bert Wang-Chak Chan. Flow-lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization. In Artificial Life Conference Proceedings 35, volume 2023, page 131.
\nMIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info . . . , 2023.<\/p>\n
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish
\nSastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from
\nnatural language supervision. In International conference on machine learning, pages 8748\u20138763. PMLR, 2021.<\/p>\n
Ettore Randazzo and Alexander Mordvintsev. Biomaker ca: a biome maker project using cellular automata.
\narXiv preprint arXiv:2307.09320, 2023.<\/p>\n
Chris Reinke, Mayalen Etcheverry, and Pierre-Yves Oudeyer. Intrinsically motivated discovery of diverse patterns in self-organizing systems. arXiv preprint arXiv:1908.06663, 2019.<\/p>\n
Craig W Reynolds. Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pages 25\u201334, 1987.<\/p>\n
Sebastian Risi. The future of artificial intelligence is self-organizing and self-assembling. sebastianrisi. com, page 14, 2021.<\/p>\n
Raymond Ros and Nikolaus Hansen. A simple modification in cma-es achieving linear time and space
\ncomplexity. In International conference on parallel problem solving from nature, pages 296\u2013305. Springer,
\n2008.<\/p>\n
J\u00a8urgen Schmidhuber. What\u2019s interesting? Technical Report IDSIA-35-97, IDSIA, 1997.
\nftp:\/\/ftp.idsia.ch\/pub\/juergen\/interest.ps.gz; extended abstract in Proc. Snowbird\u201998, Utah, 1998; see also <\/p>\n
J\u00a8urgen Schmidhuber. Powerplay: Training an increasingly general problem solver by continually searching
\nfor the simplest still unsolvable problem. Frontiers in psychology, 4:313, 2013.<\/p>\n
Jimmy Secretan, Nicholas Beato, David B D\u2019Ambrosio, Adelein Rodriguez, Adam Campbell, Jeremiah T
\nFolsom-Kovarik, and Kenneth O Stanley. Picbreeder: A case study in collaborative evolutionary explo-
\nration of design space. Evolutionary computation, 19(3):373\u2013403, 2011.<\/p>\n
Abhishek Sharma, D\u00b4aniel Cz\u00b4egel, Michael Lachmann, Christopher P Kempes, Sara I Walker, and Leroy
\nCronin. Assembly theory explains and quantifies selection and evolution. Nature, 622(7982):321\u2013328,
\n2023.<\/p>\n
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815, 2017.<\/p>\n
Sims, Karl. Evolved Virtual Creatures. ACM SIGGRAPH, 1994.<\/p>\n
Lisa Soros and Kenneth Stanley. Identifying necessary conditions for open-ended evolution through the artificial life world of chromaria. In Artificial Life Conference Proceedings, pages 793\u2013800. MIT Press One
\nRogers Street, Cambridge, MA 02142-1209, USA journals-info . . . , 2014.<\/p>\n
Kenneth O Stanley and Joel Lehman. Why greatness cannot be planned: The myth of the objective.
\nSpringer, 2015.<\/p>\n
Kenneth O Stanley, Joel Lehman, and Lisa Soros. Open-endedness: The last grand challenge you\u2019ve never heard of. While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself, 2017.<\/p>\n
Susan Stepney and Simon Hickinbotham. On the open-endedness of detecting open-endedness. Artificial
\nLife, 30(3):390\u2013416, 2024.<\/p>\n
Joseph Suarez, Yilun Du, Phillip Isola, and Igor Mordatch. Neural mmo: A massively multiagent game
\nenvironment for training and evaluating intelligent agents. arXiv preprint arXiv:1903.00784, 2019.<\/p>\n
Yunlong Tang, Jing Bi, Siting Xu, Luchuan Song, Susan Liang, Teng Wang, Daoan Zhang, Jie An, Jingyang Lin, Rongyi Zhu, et al. Video understanding with large language models: A survey. arXiv preprint
\narXiv:2312.17432, 2023.<\/p>\n
Yingtao Tian and David Ha. Modern evolution strategies for creativity: Fitting concrete images and abstract concepts. In International conference on computational intelligence in music, sound, art and design (part of evostar), pages 275\u2013291. Springer, 2022.<\/p>\n
Graham Todd, Sam Earle, Muhammad Umair Nasir, Michael Cerny Green, and Julian Togelius. Level
\ngeneration through large language models. In Proceedings of the 18th International Conference on the
\nFoundations of Digital Games, pages 1\u20138, 2023.<\/p>\n
Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. Solving olympiad geometry without
\nhuman demonstrations. Nature, 625(7995):476\u2013482, 2024.<\/p>\n
Jeffrey Ventrella. Clusters (lifelike particle systems). https:\/\/www.ventrella.com\/Clusters\/, 2017. Accessed: 2024-09-30.<\/p>\n
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, and Kenneth Stanley. Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions. In International conference on machine learning, pages 9940\u20139951. PMLR, 2020.<\/p>\n
Stephen Wolfram. A class of models with the potential to represent fundamental physics. arXiv preprint
\narXiv:2004.08210, 2020.<\/p>\n
Stephen Wolfram and M Gad-el Hak. A new kind of science. Appl. Mech. Rev., 56(2):B18\u2013B19, 2003.
\nMirek W\u00b4ojtowicz. Cellular automaton rules lexicon \u2014 family: Life, 2001. URL http:\/\/www.mirekw.com\/
\nca\/rullex_life.html. Accessed: 2024-12-07.<\/p>\n
Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettle-
\nmoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021.<\/p>\n
Larry Yaeger et al. Computational genetics, physiology, metabolism, neural systems, learning, vision, and behavior or poly world: Life in a new context. In SANTA FE INSTITUTE STUDIES IN THE SCIENCES OF COMPLEXITY-PROCEEDINGS VOLUME-, volume 17, pages 263\u2013263. ADDISON-WESLEY PUB-
\nLISHING CO, 1994.<\/p>\n
Jenny Zhang, Joel Lehman, Kenneth Stanley, and Jeff Clune. Omni: Open-endedness via models of human
\nnotions of interestingness. arXiv preprint arXiv:2306.01711, 2023.<\/p>\n
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586\u2013595, 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"
https:\/\/arxiv.org\/pdf\/2412.17799 References Wendy Aguilar, Guillermo Santamar\u00b4\u0131a-Bonfil, Tom Froese, and Carlos Gershenson. The past, present, and future of artificial life. Frontiers in Robotics and AI, 1:8, 2014. Jyrki Alakuijala, James Evans, Ben Laurie, Alexander Mordvintsev, Eyvind Niklasson, Ettore Randazzo, Luca Versari, et al. Computational life: How well-formed, self-replicating programs emerge from simpleinteraction. arXiv preprint arXiv:2406.19108, 2024. […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5235","post","type-post","status-publish","format-standard","hentry","category-uncategorised"],"yoast_head":"\n