In closing Artificial Life’s 30th anniversary volume, we would like to take the opportunity to look ahead. Some readers may feel that the field’s emergent wandering is desirable. It has been interesting (and fun!) most of the time, and valuable some of the time. Who knows what may come of our field in the future? Maybe yet more novelty will emerge. If we wander like a flock of boids looking to our nearest neighbors, addressing the interests of connected practitioners, without oversight or planning, where are we all likely to end up? We may travel toward somewhere even more interesting. Or, like a glider on Conway’s grid, we may find ourselves reinvented, in very much the same state as we were 30 years ago, but somewhere new on a grid that has changed deeply around us. Given what we Artificial Life researchers know about successful search of immense spaces, neither pure exploitation nor pure exploration yields the best outcome. If we do want to find ourselves located somewhere important, we need to balance exploratory wandering with agreement on the functions we will use to assess the value of new places we discover: a quality–diversity approach.
Most of us are guided by our intuition, by discussions with colleagues, by papers we read and seminars we attend. So we are not conducting a random walk or an aimless drift. But meta-level guidance of our field can still, potentially at least, aid us in shifting the field into a region where we want it to go. Some should wander off to explore in their own directions, ignoring trends and guides. That is an aspect of our field that many of us treasure. But if everybody does this all the time, we will remain a small, eccentric niche in a vast research web. The odds of our field having a centralized and substantial impact in this way seem low at best and negligible on average. If we adopt this approach for the next 30 years, we may continue to spin off valuable research while our core retains its diversity and its arguably “alternative” mind-set, exploring the unknown unknowns. This is potentially valuable. But it is not the only thing we should do.
Meta-level guidance can be provided by workshop themes, calls for articles on special issues, textbooks, PhD supervisors advising cohorts of students, competitions that motivate researchers to tackle specific problems, and position papers like this editorial. So here goes.
The Artificial Life journal should publish more translation research. We should publish work that applies the principles, understanding, and techniques of our discipline to tackle real, urgent, complex problems that impact human society and Earth’s other physical and ecological systems. Our field is full of diverse, well-read, well-educated, relatively privileged individuals. Many of us work at institutions that receive government funding derived from citizens’ taxes. We have a moral obligation to do something valuable. This is essential, especially now, to maintain fair and equitable processes of governance, access to health care and education, resource distribution, and environmental protection and sustainability under an unstable global sociopolitical situation and an increasingly unstable climate.
For Artificial Life to publish such important work as this, we first need to receive it. We know the work is out there. Sometimes it is published in the discipline where the translational impact is most keenly felt, perhaps in robotics, ecology, social science, or epidemiology. Much, much more can be done. We would welcome articles on translational topics in Artificial Life where the links between real-world impacts and our field’s fundamentals can be explored. So, we encourage you to think about how you, your collaborators, and your students can tackle societal and environmental issues for everyone’s benefit without lining the pockets of multinational tech firms. So many deep problems need to be solved immediately. How can we justify turning our backs? We would love to receive, review, and publish your most impactful research in Artificial Life. Send it along!
Of course, our views on where Artificial Life ought to go are just two of many. In this issue, Beer offers his personal reflections on Artificial Life’s past, and an opinion on the field’s potential directions, in “(A)Life as It Could Be.” Another of our long-term researchers, Harvey, holds views partly in opposition to those of Beer. He has expressed these in a “Comment on Randall D. Beer’s ‘A(Life) as It Could Be.’ ” We, like these authors, hope their contrasting texts stir up some debate.
One of the strengths of the discipline is the wide range of knowledge and techniques that can be brought to bear on problems: ALife provides inspiration and tools both for rich construction media and for a wide range of design approaches. Several articles in this issue demonstrate that breadth of approach.
Rusin and Medvet provide insight into one of the major themes of ALife, embodied intelligence as demonstrated in robots, in their article “How Perception, Actuation, and Communication Impact the Emergence of Collective Intelligence in Simulated Modular Robots.” They take an evolutionary approach to design, using modularity to restrict the design and optimization search spaces. Practical robots need to be simple to build but also highly functional; the authors show how these competing objectives may be achieved.
Dubey et al. are also interested in optimal design of engineered structures, here using a case study of bridge trusses. In “Evolving Novel Gene Regulatory Networks for Structural Engineering Designs,” they apply an evo-devo approach as an improved way to automate the search for better structural engineering designs. They exploit many of the techniques of ALife—evo-devo, GRNs, neural networks, and genetic programming—to develop an approach applicable to both 2-D and 3-D structures.
Continuing with the theme of embodied agent control, Langer and Ay present “Outsourcing Control Requires Control Complexity.” Here the focus is on one of the promised features of embodiment: the capability to outsource some complexity, through interactions between agent body and environment. The authors demonstrate that effective outsourcing still requires enough complexity in the controller to exploit the environmental capabilities.
In his article “Emergence and Criticality in Spatiotemporal Synchronization: The Complementarity Model,” Scirè investigates the core ALife concepts of emergence and self-organization through the lens of core ALife tools, criticality and dissipative dynamical systems, here with N coupled 2-D oscillators. The spatiotemporal dynamics of the studied system exhibit a number of emergent properties, including a variety of power law–style avalanches. The author notes how these fundamental properties may have application in some origin of life theories.
An important feature of translating from abstract models to the real world is to remember the noise and limited precision of the latter. Thresholding and fuzzy limits are a way to model such constraints. Lawry, in his article “Heterogeneous Thresholds, Social Ranking, and the Emergence of Vague Categories,” applies these ideas to social decision-making scenarios via learning fuzzy categories.
In a submission close to Artificial Life’s central idea to shed light on biology via modeling, Bull provides “On Recombination.” His letter offers a potential explanation of the relationship between the evolution of sex, recombination via meiosis, and the fitness benefits for individuals gained due to the resultant smoothing of the fitness landscape that it generates.
ALife researchers have long sought to understand, model, and potentially realize cognition by creating embodied agents. This interest predates the field’s formal inception, and Braitenberg’s (1984) Vehicles provided something of a cybernetics-originated prototype in this area, published well before the first conference in Artificial Life (Langton, 1989) and prior to the journal’s first issue, yet still influential today. In “New Directions (and Insights) in Braitenberg Vehicles and Cognitive Science,” Alicea reviews Hotton and Yoshimi’s recent book, The Open Dynamics of Braitenberg Vehicles, which takes that early work in a new direction.
Following in this established line of inquiry, Adami, in “How Brains Perceive the World,” focuses, for the fourth installment of his five-essay series, on building a sense of timing and an attention mechanism in artificial brains.
We hope that you enjoy this issue, and the next 30 volumes, of Artificial Life.