Editorial: Introduction to the 2020 Conference on Artificial Life Special Issue

Abstract

This special issue highlights key selections from the 2020 Conference on Artificial Life, which is the primary international meeting organized yearly by the International Society for Artificial Life (www.alife.org). The conference themes broadly address the synthesis and simulation of living systems, welcoming scientific research that either deepens our understanding of life as we know it or broadens our conception of life as it could be (Langton, 1989).The 2020 conference, hosted by the University of Vermont and the Vermont Complex Systems Center, was originally intended to be held in Montréal, Québec, Canada. However, the global COVID-19 pandemic forced this event, like many others, to be held online. In truth, this challenge afforded a unique opportunity to hold a truly global conference, with 390 registered attendees from around the world.Of 183 submissions, 75 articles (41%) were accepted for full presentations at the conference and published in the proceedings (Bongard et al., 2020). An additional 11 submissions were presented as lighting talks and 24 as posters and were also included in the proceedings.Reflecting the highly interdisciplinary nature of the field, the topics covered in this special issue include evolutionary dynamics, artificial chemistry, agent-based modelling, game theory, genetic programming, neuroevolution, embodiment, and complex systems research: Ghouri, Barnes, and Lewis present a minimal version of the classic river crossing task, isolating the core task of building a bridge in a grid world for the purpose of increasing explainability of the original problem. Results with the minimal environment are consistent with results from the original version, highlighting the utility of the minimal environment for experiments on explainable evolutionary intelligence.Grove, Timbrell, Jolley, Polack, and Borg demonstrate that the mathematical color of noise in an environment has a significant impact on dynamics in evolving populations. In particular, their results call into question whether commonly employed Gaussian or white noise models should be the default.Lexicase selection in genetic programming is an alternative to traditional parent selection. Helmuth and Spector conduct an extensive benchmarking of a variant called down-sampled lexicase selection, showing that it outperforms standard selection, and investigate hypotheses about why it performs so well.Howison, Hugues, and Iida explore how morphology can be used to control and program interactions with the environment in their study of V-shaped falling papers. They also show how Bayesian optimization can be used to design functional constructs of nonliving materials in the real world.Hudcová and Mikolov provide a framework for classifying cellular automata complexity based on transients, which are parts of automata trajectories observed before entering into a loop. In particular, the presented classification is based on the asymptotic growth of the average transient length with increasing grid size. This framework is intended to aid the identification of interesting phenomena in evolving systems.Krellner and Han study the evolution of cooperation among agents playing a donation game. In particular, this work introduces a novel approach to information sharing to solve the problem of private information.Kruszewski and Mikolov develop an artificial chemistry based on combinatory logic, showing that complex structures emerge over time from a simple dynamical system. This work explicitly addresses the origins of open-ended evolution, which is a longstanding pursuit for the field of Artificial Life (and science in general).Miller re-envisions the artificial neuron model to better reflect natural evolution and development processes. Using this model, evolved programs can construct artificial neural networks that can be broken down into smaller networks that each solve distinct tasks.The 2020 conference theme “New frontiers in AI: What can ALife offer AI?” asked the community to consider how the unbridled and sometimes unconventional creativity of Artificial Life research might inspire innovation in mainstream Artificial Intelligence. In fact, the two fields share a deeply intertwined history, as some of the greatest pioneers in early Artificial Intelligence work also (or first) pursued what would now be called Artificial Life. As an example, Shannon (1940), often referred to as the father of information theory, wrote his doctoral dissertation An Algebra for Theoretical Genetics 16 years before he helped found the field of Artificial Intelligence at the Dartmouth Conference.At the same time, the Artificial Life community continues in its own myriad pursuits, recapitulating and reinventing nature often with computational tools, as evidenced by the works contained in this volume. Evolution on Earth gave rise to natural intelligence, and so evolution in silico (a mainstay of Artificial Life research) similarly bears the potential for creating Artificial Intelligence open-endedly; such is the foundational assumption of research on open-ended evolution. What ALife can offer AI is, among other things, an invitation to question what about life and intelligence might transcend substrates and, in doing so, to discover how what is might inspire what will be.