Artificial Life Next Generation Perspectives: Echoes from the 2018 Conference in Tokyo

摘要

Artificial life is a research field devoted to the theoretical study of features of living systems, such as evolution and the brain. The field has developed philosophical concepts such as autopoiesis and emergence, alongside a large range of computational and experimental setups, from evolutionary simulations to robotics and chemical experiments.The complexity and diversity of the artificial life field is crucial to its community. Many researchers consider the community a real source of creativity and free-minded exchange of ideas on important questions. For ideas that don't fit neatly into a single “mainstream” field of science, there is value in examining and discussing them in a context free from departmental or disciplinary constraints, with the purpose of reaching a better knowledge of the fundamental mechanisms that govern living systems.Workers in the field of artificial life convene yearly at a conference, which most recently took place in 2018 in Tokyo. The ALIFE 2018 conference highlighted eight categories of topics:• Perception, cognition, behavior• Bio-inspired, cognitive, and evolutionary robotics; swarms• Ecological and social systems• Artificial chemistry, origins of life, computational biology• Complex dynamical systems and networks• Evolution of language, computational linguistics• Philosophy of mind, philosophy of science• Artificial-life–based art• Synthetic biology and wet artificial life• Education and society issuesIn this issue, we present a selection of recent studies on these topics, which were presented at ALIFE 2018 and which reflect the very rich heterogeneity of the field, and many of which are contributions from young scientists. These articles have been selected from a very diverse pool of submissions. The conference featured 112 papers, selected out of 212 submissions, of which 64 were accepted as oral presentations and 48 as poster presentations.We focused on top-rated, promising, and impactful studies by young contributors to artificial life research. The articles cover a set of concepts key to artificial life, and ground them in concrete models and outputs to better describe those concepts and make them actionable for the whole field. A simple classification of the current research paradigm in this volume can be described as follows: (i) autopoiesis, (ii) evolution and evolvability, (iii) emergence, and (iv) embodiment.The first three articles examined autopoiesis and embodiment. Randall Beer [1] investigated the initial-state dependence of the Game of Life to show how the initial distribution generates gliders but also annihilates and regenerates them as time passes. This is a prototypical life evolution example. This work is in the line of embodied autopoiesis, which is reconsidered by studying gliders and their peripheral configurations.Tamaru, Yui, and Hashida [9] demonstrated the self-organized motion of a pine cone, emphasizing its unexpected locomotion, which realizes a sensory-motor coupling. The motion of gliders and pine cones is different, but has that common property, resulting from environmental conditions (humidity for pine cones, and the bit configuration for gliders).Masumori et al. [5] discussed the concept of autopoiesis in terms of artificial and biological neural focused on networks. Their work proposes a new learning principle of neural networks called stimulus avoidance. If a network can avoid being stimulated from the outside, it does so by organizing its behavior to avoid the source of stimuli in the environment. If not, the network tends to inactivate its sensory part to prevent the information from the sensor neurons from spreading. So, effectively, the sensory inputs are removed.In contrast with Randy Beer's work [1], neural networks can self-organizingly change their boundary conditions to acquire robustness. A neural network protects itself from environmental stimuli, whereas gliders are totally fragile against external perturbation.The next articles examined aspe