Authors:Dorin A; Stepney S. Pages: 73 - 74 Abstract: It has been almost 20 years, and 20 volumes, since Mark Bedau took on the role of Editor in Chief of Artificial Life. After diligently maintaining the journal day-to-day and steering it strategically over the long term, Mark has decided to retire from this role. He was instrumental in cementing the Artificial Life journal as a dependable and valuable publication venue for research in our field. But also, during his lengthy term, Mark was willing to trust would-be guest editors with the independence to manage their own special issues. He remained consistently keen to innovate and to explore new directions for the journal that capitalised on our field’s inherent multidisciplinarity, curiosity, and diversity. PubDate: Tue, 02 Nov 2021 00:00:00 GMT DOI: 10.1162/artl_e_00350 Issue No:Vol. 27, No. 2 (2021)
Authors:Bull L. Pages: 75 - 79 Abstract: AbstractThe significant role of dendritic processing within neuronal networks has become increasingly clear. This letter explores the effects of including a simple dendrite-inspired mechanism into neuro-evolution. The phenomenon of separate dendrite activation thresholds on connections is allowed to emerge under an evolutionary process. It is shown how such processing can be positively selected for, particularly for connections between the hidden and output layers, and increases performance. PubDate: Tue, 02 Nov 2021 00:00:00 GMT DOI: 10.1162/artl_a_00338 Issue No:Vol. 27, No. 2 (2021)
Authors:Minh-Thai T; Samarasinghe S, Levin M. Pages: 80 - 104 Abstract: AbstractMany biological organisms regenerate structure and function after damage. Despite the long history of research on molecular mechanisms, many questions remain about algorithms by which cells can cooperate towards the same invariant morphogenetic outcomes. Therefore, conceptual frameworks are needed not only for motivating hypotheses for advancing the understanding of regeneration processes in living organisms, but also for regenerative medicine and synthetic biology. Inspired by planarian regeneration, this study offers a novel generic conceptual framework that hypothesizes mechanisms and algorithms by which cell collectives may internally represent an anatomical target morphology towards which they build after damage. Further, the framework contributes a novel nature-inspired computing method for self-repair in engineering and robotics. Our framework, based on past in vivo and in silico studies on planaria, hypothesizes efficient novel mechanisms and algorithms to achieve complete and accurate regeneration of a simple in silico flatwormlike organism from any damage, much like the body-wide immortality of planaria, with minimal information and algorithmic complexity. This framework that extends our previous circular tissue repair model integrates two levels of organization: tissue and organism. In Level 1, three individual in silico tissues (head, body, and tail—each with a large number of tissue cells and a single stem cell at the centre) repair themselves through efficient local communications. Here, the contribution extends our circular tissue model to other shapes and invests them with tissue-wide immortality through an information field holding the minimum body plan. In Level 2, individual tissues combine to form a simple organism. Specifically, the three stem cells form a network that coordinates organism-wide regeneration with the help of Level 1. Here we contribute novel concepts for collective decision-making by stem cells for stem cell regeneration and large-scale recovery. Both levels (tissue cells and stem cells) represent networks that perform simple neural computations and form a feedback control system. With simple and limited cellular computations, our framework minimises computation and algorithmic complexity to achieve complete recovery. We report results from computer simulations of the framework to demonstrate its robustness in recovering the organism after any injury. This comprehensive hypothetical framework that significantly extends the existing biological regeneration models offers a new way to conceptualise the information-processing aspects of regeneration, which may also help design living and non-living self-repairing agents. PubDate: Tue, 02 Nov 2021 00:00:00 GMT DOI: 10.1162/artl_a_00343 Issue No:Vol. 27, No. 2 (2021)
Authors:Peña E; Sayama H. Pages: 105 - 112 Abstract: AbstractCellular automata (CA) have been lauded for their ability to generate complex global patterns from simple local rules. The late English mathematician, John Horton Conway, developed his illustrious Game of Life (Life) CA in 1970, which has since remained one of the most quintessential CA constructions—capable of producing a myriad of complex dynamic patterns and computational universality. Life and several other Life-like rules have been classified in the same group of aesthetically and dynamically interesting CA rules characterized by their complex behaviors. However, a rigorous quantitative comparison among similarly classified Life-like rules has not yet been fully established. Here we show that Life is capable of maintaining as much complexity as similar rules while remaining the most parsimonious. In other words, Life contains a consistent amount of complexity throughout its evolution, with the least number of rule conditions compared to other Life-like rules. We also found that the complexity of higher density Life-like rules, which themselves contain the Life rule as a subset, form a distinct concave density-complexity relationship whereby an optimal complexity candidate is proposed. Our results also support the notion that Life functions as the basic ingredient for cultivating the balance between structure and randomness to maintain complexity in 2D CA for low- and high-density regimes, especially over many iterations. This work highlights the genius of John Horton Conway and serves as a testament to his timeless marvel, which is referred to simply as: Life. PubDate: Tue, 02 Nov 2021 00:00:00 GMT DOI: 10.1162/artl_a_00348 Issue No:Vol. 27, No. 2 (2021)
Authors:St. Luce S; Sayama H. Pages: 113 - 130 Abstract: AbstractThe El Farol Bar problem highlights the issue of bounded rationality through a coordination problem where agents must decide individually whether or not to attend a bar without prior communication. Each agent is provided a set of attendance predictors (or decision-making strategies) and uses the previous bar attendances to guess bar attendance for a given week to determine if the bar is worth attending. We previously showed how the distribution of used strategies among the population settles into an attractor by using a spatial phase space. However, this approach was limited as it required N − 1 dimensions to fully visualize the phase space of the problem, where N is the number of strategies available.Here we propose a new approach to phase space visualization and analysis by converting the strategy dynamics into a state transition network centered on strategy distributions. The resulting weighted, directed network gives a clearer representation of the strategy dynamics once we define an attractor of the strategy phase space as a sink-strongly connected component. This enables us to study the resulting network to draw conclusions about the performance of the different strategies. We find that this approach not only is applicable to the El Farol Bar problem, but also addresses the dimensionality issue and is theoretically applicable to a wide variety of discretized complex systems. PubDate: Tue, 02 Nov 2021 00:00:00 GMT DOI: 10.1162/artl_a_00347 Issue No:Vol. 27, No. 2 (2021)
Authors:Čejková J. Pages: 138 - 140 Abstract: Rise of the Self-Replicators: Early Visions of Machines, AI and Robots That Can Reproduce and Evolve. By TaylorTim and DorinAlan. (2020, Springer International Publishing). Softcover, xiv, 121 Pages. PubDate: Tue, 02 Nov 2021 00:00:00 GMT DOI: 10.1162/artl_r_00345 Issue No:Vol. 27, No. 2 (2021)
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