--- layout: default --- Publication details Evolving Interaction in Artificial Systems: An historical overview and future directions Tim Taylor 2003 Abstract Over the last 50 years, a growing number of attempts have been made to engineer systems which can support the open-ended evolution of self-replicating components. This work includes not just software systems, but also those employing robotic, electronic, and chemical media. Much of this work has been (explicitly or implicitly) based upon the neo-Darwinist premise that the existence of living organisms can fundamentally be explained in terms of the processes of reproduction, variation and natural selection. However, the stark fact is that none of these attempts to recreate such processes in an artificial system has succeeded in producing an ongoing evolutionary process. Rather than producing ever more complex organisms/societies/ecologies, evolution in these systems generally leads to a stable and simple end point in which only the smallest and fastest reproducers survive. These results suggest that the neo-Darwinist position is, at best, only a partial explanation of the evolution of complexity. Commenting on one such attempt (Orgel’s experiments with the in vitro evolution of RNA sequences), John Maynard Smith remarks: "This raises the following simple, and I think unanswered, question: What features must be present in a system if it is to lead to indefinitely continuing evolutionary change?" [Maynard Smith 88]. Even going back to the earliest attempts to engineer such systems, the importance of interactions between organisms for providing co-evolutionary selection pressure for increased complexity has been recognised as a key issue [Barricelli 62; Conrad & Pattee 70]. From a biological perspective, Waddington too recognised the need for elaborating the necessary and sufficient conditions under which open-ended evolution might arise [Waddington 69]. His proposal included the requirement for "an indefinite number of environments, and this is assured by the fact that the evolving phenotypes are components of environments for their own or other species." One of the major challanges facing designers of artificial evolutionary systems – and one which remains largely unsolved – is to understand how to design systems in which indefinitely new types of inter-organism interaction can evolve. From Waddington’s perspective, this can also be stated as the need to understand how to design systems in which evolving organisms are fully part of the environment experienced by other organisms. These matters concern the fundamental design issue of the relationship between organisms and their environment. The nature of the interface between organism and environment determines the organism’s potential for sensing, acting, and communicating with its environment (which may include other organisms). Furthermore, the capacity for this interface to evolve determines the potential for new forms of inter-organism interaction to evolve. In this talk, an overview of the work described above will be presented, together with a discussion of the (often very limited) capacity of various well-known artificial evolutionary systems (both software- and hardware-based) to evolve new forms of interaction. Drawing from the experiences of these existing studies, the talk will conclude with some suggestions for improving the design of future artificial evolutionary systems in order to increase their evolutionary potential. Full text Presentation slides: pdf Reference Taylor, T. (2003). Evolving Interaction in Artificial Systems: An historical overview and future directions. In P. McOwan, K. Dautenhahn, & C. L. Nehaniv (Eds.), Abstracts from the Evolvability and Interaction Symposium, held at Queen Mary, University of London, UK, in October 2003. University of Hertfordshire Computer Science Technical Report No. 393. BibTeX @incollection{taylor2003evolving, author = {Taylor, Tim}, title = {Evolving Interaction in Artificial Systems: An historical overview and future directions}, booktitle = {Abstracts from the Evolvability and Interaction Symposium, held at Queen Mary, University of London, UK, in October 2003}, publisher = {University of Hertfordshire Computer Science Technical Report No.~393}, year = {2003}, month = oct, editor = {McOwan, Peter and Dautenhahn, Kerstin and Nehaniv, Chrystopher L.}, category = {workshop}, keywords = {evoca, history, oee} } Related publications
  1. Channon, A., Bedau, M., Packard, N., & Taylor, T. (2024). Editorial Introduction to the 2024 Special Issue on Open-Ended Evolution. Artificial Life, 30(3), 300–301. https://doi.org/10.1162/artl_e_00445
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  2. Taylor, T. (2024). An Afterword to "Rise of the Self-Replicators": Placing John A. Etzler, Frigyes Karinthy, Fred Stahl, and Others in the Early History of Thought About Self-Reproducing Machines. Artificial Life, 30(1), 91–105. https://doi.org/10.1162/artl_a_00424
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  3. Taylor, T. (2021). Evolutionary Innovation Viewed as Novel Physical Phenomena and Hierarchical Systems Building. Presented at the Fourth Workshop on Open-Ended Evolution (OEE4) at the 2021 Conference on Artificial Life (ALIFE 2021). Retrieved from https://arxiv.org/abs/2107.09669
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  4. Taylor, T. (2020). The Importance of Open-Endedness (For the Sake of Open-Endedness). In J. Bongard, J. Lovato, L. Hebert-Dufrésne, R. Dasari, & L. Soros (Eds.), ALIFE 2020: Proceedings of the Artificial Life Conference 2020 (pp. 578–580). https://doi.org/10.1162/isal_a_00257
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  5. Taylor, T., & Dorin, A. (2020). Rise of the Self-Replicators: Early Visions of Machines, AI and Robots That Can Reproduce and Evolve. Cham: Springer.
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  6. Taylor, T. (2019). Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems. Artificial Life, 25(2), 207–224. https://doi.org/10.1162/artl_a_00290
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  7. Packard, N., Bedau, M., Channon, A., Ikegami, T., Rasmussen, S., Stanley, K., & Taylor, T. (2019). An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue. Artificial Life, 25(2), 93–103. https://doi.org/10.1162/artl_a_00291
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  8. Packard, N., Bedau, M., Channon, A., Ikegami, T., Rasmussen, S., Stanley, K., & Taylor, T. (2019). Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution I Special Issue. Artificial Life, 25(1), 1–3. https://doi.org/10.1162/artl_e_00282
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  9. Taylor, T., & Dorin, A. (2018). Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines. In T. Ikegami, N. Virgo, O. Witkowski, M. Oka, R. Suzuki, & H. Iizuka (Eds.), ALIFE 2018: Proceedings of the Artificial Life Conference 2018 (pp. 91–98). https://doi.org/10.1162/isal_a_00022
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  10. Taylor, T. (2018). Routes to Open-Endedness in Evolutionary Systems. Presented at the Third Workshop on Open-Ended Evolution (OEE3) at the 2018 Conference on Artificial Life (ALIFE 2018). Retrieved from https://arxiv.org/abs/1806.01883v3
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  11. Taylor, T., Bedau, M., Channon, A., Ackley, D., Banzhaf, W., Beslon, G., … Wiser, M. (2016). Open-Ended Evolution: Perspectives from the OEE Workshop in York. Artificial Life, 22(3), 408–423. https://doi.org/10.1162/artl_a_00210
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  12. Taylor, T. (2015). Requirements for Open-Ended Evolution in Natural and Artificial Systems. Presented at the EvoEvo Workshop at the European Conference on Artificial Life 2015 (ECAL 2015). Retrieved from https://arxiv.org/abs/1507.07403
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  13. Taylor, T., Dorin, A., & Korb, K. (2014). Digital Genesis: Computers, Evolution and Artificial Life. Presented at the 7th Munich-Sydney-Tilburg Philosophy of Science Conference: Evolutionary Thinking, University of Sydney, 20-22 March 2014. Retrieved from https://arxiv.org/abs/1512.02100
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  14. Taylor, T. (2014). Evolution in virtual worlds. In M. Grimshaw (Ed.), The Oxford Handbook of Virtuality (pp. 526–548). https://doi.org/10.1093/oxfordhb/9780199826162.013.044
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  15. Taylor, T. (2012). Exploring the Concept of Open-Ended Evolution. In C. Adami, D. M. Bryson, C. Ofria, & R. T. Pennock (Eds.), Artificial Life 13: Proceedings of the Thirteenth International Conference on the Simulation and Synthesis of Living Systems (pp. 540–541). MIT Press.
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  16. Taylor, T. (2004). Redrawing the Boundary between Organism and Environment. In J. Pollack, M. A. Bedau, P. Husbands, R. A. Watson, & T. Ikegami (Eds.), Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (pp. 268–273). https://doi.org/10.7551/mitpress/1429.003.0045
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  17. Taylor, T. (2003). Sensor Evolution in Artificial Systems: Towards a more appropriate model of the relationship between organism and environment. In J. F. Miller, D. Polani, & C. L. Nehaniv (Eds.), Abstracts from the Evolvability and Sensor Evolution Symposium, held at University of Birmingham, UK, in April 2003. University of Hertfordshire Computer Science Technical Report No. 384.
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  18. Taylor, T. (2002). An Alternative Approach to the Synthesis of Life. Poster presented at the 8th International Conference on the Simulation and Synthesis of Living Systems (ALIFE 8), Sydney, Australia.
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  19. Taylor, T. (2002). The Control of Dynamical Systems by Evolved Constraints: A New Perspective on Modelling Life (Informatics Research Report No. EDI-INF-RR-0148). University of Edinburgh.
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  20. Taylor, T. (2001). Creativity in Evolution: Individuals, Interactions and Environments. In P. J. Bentley & D. W. Corne (Eds.), Creative Evolutionary Systems (pp. 79–108). https://doi.org/10.1016/b978-155860673-9/50037-9
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  21. Taylor, T. (2000). Some Representational and Ecological Aspects of Evolvability. In C. L. Nehaniv (Ed.), Proceedings of the Evolvability Workshop at the the Seventh International Conference on the Simulation and Synthesis of Living Systems (Artificial Life 7) (pp. 41–44). Retrieved from http://homepages.herts.ac.uk/ comqcln/al7ev/cnts.html
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  22. Taylor, T. J. (1999). From Artificial Evolution to Artificial Life (PhD thesis). School of Informatics, College of Science and Engineering, University of Edinburgh.
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  23. Taylor, T. (1998). Nidus Design Document (Departmental Working Paper No. 269). Department of Artificial Intelligence, University of Edinburgh.
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