Abstract Field-captured video enables detailed study of animal locomotion, decision-making, and environmental interactions such as predator–prey dynamics and habitat use. While low-cost hardware makes data capture accessible, the storage, processing, and transmission demands of high-resolution video remain a major hurdle for field-deployed edge computing devices. Motion tracking in natural environments presents unique challenges that require tailored video compression strategies not well addressed in other domains. We present a novel end-to-end system comprising a motion analysis-based video compression algorithm specifically designed for camera traps, and a custom video processing methodology for automated analysis of compressed footage to extract behavioural data. We evaluate it through a case study on insect–pollinator motion tracking using three popular edge computing platforms. The compression algorithm operates alongside standard codecs, identifying and storing only image regions containing motion relevant to monitoring tasks, reducing data size by an average of 87% across diverse datasets. When combined with the H.265/HEVC codec, our approach achieved an additional 47.1% improvement in compression compared to stand-alone H.265. The accompanying video processing algorithm builds upon existing Polytrack software, incorporating new preprocessing and trajectory reconstruction techniques for automated processing of compressed footage with a 97.5% detection rate. Our experiments demonstrate that the system retains critical behavioural information, as verified through both automated and manual analyses. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses.

The field called Artificial Life (ALife) coalesced following a workshop organized by Chris Langton in September 1987 (Langton, 1988a). That meeting drew together work that had been largely carried out from the 1950s through to the 1980s. A few years later, Langton became the founding editor of this journal, Artificial Life, which started its life with Volume 1, Issue 1_2 in the (northern) winter of 1993/1994.1 This current issue therefore begins the 30th volume and 30th year of Artificial Life. We think this is a milestone worth celebrating!In the proceedings of that first workshop, Langton famously defined ALife as the study of “life as it could be,” of “possible life,” in contrast to biology’s study of “life as we know it to be” (on Earth). His stated aim was to derive “a truly general theoretical biology capable of making universal statements about life wherever it may be found and whatever it may be made of ” (Langton, 1988b, p. xvi).Central to ALife is a debate that has continued sporadically since the field’s formal inception. This explores what should be properly counted as living phenomena and what should not. As a field, ALife has sustained a diverse range of opinions on this topic and has simultaneously investigated even contradictory perspectives. As the field gained attention in the mid-1990s, this was partially responsible for criticism that ALife had a lack of focus, a looseness with metaphor and the use of terminology, and a lack of grounding in fundamental biology (Horgan, 1995; Smoliar, 1995). Such risks were explicitly preempted by Nils Aall Barricelli (1962) as he pioneered digital evolution in the 1950s and 1960s. Similar criticisms, however, have also been leveled at biology for failing to agree on the necessary and sufficient characteristics, hence a definition, of life itself. This is something considered even by Aristotle; countless books, including those by Schrödinger (1944), Monod (1971), Rosen (1991), and Morange (2008), have been written on the subject in the 2,300 years since that time. Hence it is hardly surprising that ALife, a relative newcomer to the debate, has struggled with the same issue, especially because it proposes to broaden biology’s definition, whatever that may be.Possibly as a result of ALife’s diversity, the field resembles more a tangled bramble of subdisciplines than some neat “Tree of Life” (Figure 1). Most of its current topics build directly on work published in the earliest workshop proceedings (Langton, 1988a; Langton et al., 1992) and the first double issue of this journal. Indeed, we recommend that newcomers to the discipline, and even more established members, (re-)read those early works. They reveal that the relationships between the topics were as tangled then as they are now. Arguably, this is because tight classification seems less interesting to the community as a whole than exploring links between fields; the nodes of this complex network are less important than its edges.We venture to claim that ALife is an outlet for the parts of ourselves that are “scientific misfits,” the parts that do not fit naturally into a single academic discipline. This does not undermine the field’s validity, the quality of its practitioners, or the rigor with which ALife research can be conducted; rather, it highlights a strength, namely, that we cherish the opportunity to be creative, to think outside the silos that today’s science can inflict regarding “value”: departmental foci and territorial stakes, government funding priorities, industry needs, financial reward, initiation of hot topics and trends, promotion paths, employment opportunities, and publication citation metrics. ALife is arguably not a straightforward way to meet such goals.We have found anecdotally during decades of ALife conferences, meetings, and email exchanges that a diversity of views and explorations is a key feature that maintains individuals’ interest in the ALife research community. As a community, we prize novelty, innovation, and open-endedness in approaches to our work as much as we prize them in the outcomes of our research. We have also been historically accepting of a wide range of opinions, have been willing to openly debate them, and have welcomed explorations made by hobbyists or professional academics in their spare time beyond that devoted to “serious” research in traditional and better-supported fields. As a result, ALife remains fresh and fun at 30 plus years, and attendance at an ALife conference can be as exciting as entering a new field, even for those of us who have been involved a long time.But, if the field is so diverse, does it have a common goal? Yes, and this does not seem to have changed much in the last 30 years, as the word cloud from 1993–1994 (Figure 1) illustrates. We would add, though, that it is not simply to study life as it could be using a diversity of approaches; it is also to design it, build it, play with it, and enjoy it from a variety of perspectives. The playful aspect has been criticized repeatedly, especially but not exclusively in the contexts of synthetic biology and cloning, where the manipulated media match those of natural biology. One criticism leveled at practitioners in this subdomain is a perceived need to “play God” (Douglas et al., 2013), usually a male one (throughout Helmreich, 1998), with Frankensteinian connotations of upsetting the natural order (Kember, 2003, p. 56). There may be some elements of this in ALife. But our (one male and one female) editors’ sense of the dominant engagement between world builder and worlds here is one of curiosity and wonder, rather than a drive for control or mastery. As pointed out in some accounts of the God-likeness of ALife world builders (Kember, 2003, p. 97), a lack of control is in fact frequently encountered; it may even be desirable (Brooks & Flynn, 1989; Kelly, 1994). Hence the sense of exploration and playfulness not only endures; arguably, it is fundamental.Playfulness is something we frequently see in submissions to the journal. In fact, we wish that some projects and submissions were less “tinkering” and better structured! We need our journal’s submissions to follow at least the main requirements of rigorous science, including well-stated hypotheses; clear context setting; reproducability; careful analysis; and detailed exploration of outcomes, benefits, and their implications. Yet even in the more formal submissions, playfulness and enjoyment need not be diminished; they underpin thoughtful and well-contextualized research in a collective and individual effort to (a) understand the aspects of biological, ecological, and societal systems that facilitate or generate interactions or behaviors typically understood to be characteristics of living systems and (b) replicate the interactions and behaviors of life in media other than those from which natural organisms are constructed.Ultimately, then, our journal’s scope remains more or less constant after 30 years. Studies of life’s behaviors and interactions realized through technology are always welcome. The media might change. There may remain long-standing disagreement in the community whether something is “really alive” (Pattee, 1988). There is sometimes a tendency even to ignore or set aside this question and delve recklessly into biological metaphors; we have been warned repeatedly. But all of this has been shown over the years to matter far less to our community than whether our novel study systems behave in ways we deem to be relevant and engaging.With the design of engaging systems a high priority, it is little wonder that ALife continues to be enthusiastically embraced and broad: compare Figure 2, a title-term plot (1993–2023), and Figure 1, a keyword cloud (1993–1994). Its topics have been surveyed before (Aguilar et al., 2014), even since the early days (Farmer & Belin, 1992), but here we attempt to manage ALife’s bramble of subdisciplines with a simple taxonomy and 30 years’ hindsight. We then discuss the articles published in this issue of the journal, before concluding with a discussion of some important ethical and practical concerns for future ALife technologies.Today’s interdisciplinary study of ALife covers a variety of aspects.2 Not all researchers in these areas would consider themselves to be studying ALife per se; nevertheless, here we describe a simple taxonomy of research topics in this context. As noted, these topics do not form a neat hierarchical tree but rather form a tangled meshwork of interconnected and interrelated aspects that encompass ALife activities. Our classification is not exhaustive. A complete and detailed scope would be short-lived, because the field shifts with changes in technology, understanding, and culture; its boundaries are ill defined, and as subareas mature, they may calve off, form new (sub-)disciplines; and acquire new names.This topic considers individual artificial entities that represent or behave like artificial organisms. It can be roughly broken down into three areas: wet, robotic, and virtual ALife (Bedau, 2003; Farmer & Belin, 1992).Wet ALife covers artificial entities made from “wet” chemicals and their reactions. Examples include chemical systems like motile droplets (Čejková et al., 2017); minimal protocells constructed either bottom-up, from biochemical components, or top-down, by stripping out nonvital components from natural cells (Rasmussen et al., 2009); and natural organisms modified by synthetic biology techniques, such as gene engineering (Hanczyc, 2020).Robotic ALife covers artificial entities engineered from “dry” components. The robot bodies may be “hard,” mostly rigid, stiff mechanical components with relatively few degrees of freedom, or “soft,” mostly pliable, elastic components with very many degrees of freedom (Lee et al., 2017; Yasa et al., 2023). The “brains” of these systems are often some form of neural controller implemented in a standard computer. The area includes evolutionary robotics, especially when allowing body and brain to coevolve (Doncieux et al., 2015; Nolfi & Floreano, 2000); swarm robotics, a population of homogeneous or heterogeneous robots cooperating to achieve a task, from nano-scale to macro-scale systems (Schranz et al., 2020); and cognitive robotics, focusing on the intelligence, learning, and adaptation of robots (Cangelosi & Asada, 2022).Virtual ALife covers artificial entities that exist in a virtual environment as software running on a computer or over a network of computers. The research goal is for these entities to actually live, not to be mere simulations of life (Pattee, 1988). What this means, and whether it is possible, has been a subject of some philosophical debate, especially in the early days of ALife. Life is a dynamic process; “life is a verb, not a noun” (Gilman, 1904, chap. X). Does that process need to be physically realized in carbon-based chemistry, or can it be realized in different material substrates undergoing different physical processes—or can it even be virtual? Does the matter matter? The computational viewpoint tends to say that it does not (Langton, 1986; Ray, 1992); the biological viewpoint tends to disagree (Maturana & Varela, 1980; Morange, 2008; Rosen, 1991). Pattee (1988) explored criteria for realizations of virtual life.Some of the issues around virtual ALife are sidestepped by simulated ALife, which makes no claims about the simulation being alive, and by embodied ALife, which focuses on embodiment and situatedness as necessary components of a living entity (Clark, 1997); a form of embodiment may be possible even in virtual systems (Stepney, 2007). Consideration of embodied ALife includes aspects of growth, morphogenesis, and development (Doursat et al., 2012) and more generally of self-construction, self-assembly, and autopoiesis (McMullin, 2004; Varela et al., 1974).In addition to these of the material or of the and computational aspects of the and are also of the whole living the in a through its and in to and population behaviors and and on to intelligence, all have an computational aspect (Stepney, and all of the subareas can be for in evolutionary swarm et al., & might be at the of and swarm ALife, the individuals’ bodies and swarm collective can result from swarm et al., the tangled of the that is for all parts of our not only for artificial organisms do and in complete They exist as and in of including some and in This of ALife of artificial its with artificial organisms of organisms undergoing with and in the context of use simple of these to for of their use can often have and surprising et al., natural evolution is much more in of population of from gene to gene the of development and interactions between and defined more natural of evolution are the of ALife evolutionary as of in the including the articles by et et et and also such as collective in or and & of biological like these can drive in swarm simulations can these behaviors can from only in of their other and an environment of entities and all of which of different ways of making a that can themselves and & 2008; et al., This of different to and through and & and & and et al., as the can facilitate and interactions et al., and all which to of and & and are also fundamental to biology. 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We start this issue with two research articles.The first, by Bullinaria, "Simulating the Effect of Environmental Change on Evolving Populations," uses simulations to study how populations can adapt to rapid environmental changes.It demonstrates the effect of model parameters on system robustness and the consequent care needed when modeling environmental change.The second article, by Collard, "Processionary Caterpillars at the Edge of Complexity," explores a model of the collective behavior of pine tree processionary caterpillars.The article begins by modeling the caterpillars' actual behaviors, then is extended to explore some more complex collective behaviors.Following these contributions, we include a series of articles forming a special issue of extended papers from the Artificial Life conference held online and in Trento, Italy, in 2022 (Holler et al., 2022).Thanks to Silvia Holler, Barbora Hudcová, Richard Löffler,

We start this issue with two research articles. The first is by Rainwater, on “Self-Organization and Phase Transitions in Driven Cellular Automata.” In this article, the author explores the altered dynamics of Conway’s Game of Life cellular automaton that occur when cells are forced to remain in the “on” state, which would not normally remain on if they were simply following the standard rules for state transitions. The second article is by Milano and Nolfi, on “Interaction Rules Supporting Effective Flocking Behavior.” They investigate using more complex interaction rules for the individual agents and the effect of field of view on flocking and aggregation behaviors. Somewhat counterintuitively, models with restricted sensory input from a restricted field of view are shown to aggregate more effectively than models with access to more sensory information.Following these, we include a series of articles forming a special issue on open-ended evolution (OEE), which presents work that either follows on from the fourth Workshop on OEE, held at the Artificial Life conference in Prague and online in 2021, or has been developed since then to build on the two previous special issues on OEE (Artificial Life, 25[1–2]). Alastair Channon, Mark Bedau, Norman Packard, and Tim Taylor serve here as guest editors of the special issue.

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.

Accessible generative artificial intelligence (AI) tools like large-language models (LLMs) (e.g., ChatGPT,1 Minerva2) are raising a flurry of questions about the potential and implications of generative algorithms and the ethical use of AI-generated text in a variety of contexts, including open science (Bugbee & Ramachandran, 2023), student assessment (Heidt, 2023), and medicine (Harrer, 2023). Similarly, among the graphic and visual arts communities, the use of generative image synthesis algorithms (e.g., DALL-E,3 Midjourney,4 Stable Diffusion5) that take text prompts as input and produce works in the style of a particular human artist, or no artist who ever lived, are causing consternation and posing challenging questions (Murphy, 2022; Plunkett, 2022). The use of generative AI to create deep fakes has also been in the spotlight (Ruiter, 2021), as has its role in answering scientific research questions directly (Castelvecchi, 2023).To our minds, the questions these technologies are raising do not seem to be of a fundamentally different character to questions asked about AI for many years. They largely concern (a) what is possible, (b) what is right, and (c) the implications of the technology’s use. For instance,Can AI generate documentary “evidence” that is indistinguishable from reality? Can AI generate artifacts that are competitive with (or superior to) those made by a human?How is the concept of “truth” confused or undermined by the output of these technologies? Is it ethical to load examples of a human’s art to generate a model for replicating their style?Who owns the intellectual property rights of AI-generated artifacts? Who is accountable if an AI-generated artifact causes harm? Will I lose my job to an AI; does AI make creative writers or artists redundant?The sense of urgency and public interest in these questions has increased as generative models become practical and ubiquitous tools with levels of skill or expertise that appear to be of above average human competency.Of general relevance to academic publishers, including Artificial Life, is that LLMs can compose text structured in the form of research paper sections or even whole articles. And these texts are often (currently) hard to distinguish from human-written text, even by software trained to detect text generated by language models (LMs). When questioned (on February 20, 2023) “What should a journal editor do about LMs?” You.com’s chatbot suggested,A journal editor should ensure that all submitted manuscripts adhere to the journal’s style guide, and that the language used is appropriate for the journal’s audience. They should also ensure that any language models (LMs) used to assist with the writing process are properly cited and that any generated text is labeled as such. Additionally, the journal editor should ensure that the LMs are used in accordance with the journal’s policies and ethical considerations.That’s a generic response that begins tangentially to the questioner’s intended focus. As any human reader can immediately see, the chatbot is not aware of the social context of the question, nor has it had the opportunity to gauge the questioner’s level of expertise. But the text is “good enough”—it does address relevant issues, and it’s an answer that probably took you a few seconds to read, interpret, and critique. It may take you more time to read and analyze the text and this paragraph than it took for us and the chatbot to generate it. Was this therefore a waste of your time, or ours? Is the chatbot wasting your time? Are we? Some journals and publishers have drafted formal policies that require the use of LLMs for writing submissions to be explicitly acknowledged (e.g., at Springer-Nature; “Tools Such as ChatGPT,” 2023). In practice, their use (or misuse) may be very difficult to detect.Artificial Life, and its publisher MIT Press generally, is also adopting the policy that any use of generative AI, for any part of a submitted work, including but not limited to text, images, sound, data, mathematics, logic, reasoning, programming code, or algorithms, must be prominently, explicitly, and unambiguously labeled and its source formally cited (e.g., via a name, manufacturer, URL, version number, or access date).Journals and publishers have also moved to prevent LLMs from being listed as authors on articles. For instance, Springer-Nature’s policy was online earlier this year, and, although it has now seemingly been removed from its original location, variants of it have been incorporated into the authorship policies of some journals:Large Language Models (LLMs), such as ChatGPT, do not currently satisfy our authorship criteria. Notably an attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs. Use of an LLM should be properly documented in the Methods section (and if a Methods section is not available, in a suitable alternative part) of the manuscript. (Nature, 2023)In a policy that remains online at time of writing, the journal Science states,Text generated from AI, machine learning, or similar algorithmic tools cannot be used in papers published in Science journals, nor can the accompanying figures, images, or graphics be the products of such tools, without explicit permission from the editors. In addition, an AI program cannot be an author of a Science journal paper. A violation of this policy constitutes scientific misconduct. (Science, 2023)Artificial Life and MIT Press are taking an approach in alignment with those of the editorial boards (and publishing house legal teams) of such journals: that authorship is associated with responsibility and accountability for an article. However, for Artificial Life, the issue doesn’t stop there.The implications of generative AI are relevant to a broad spectrum of society. But an interest in generative computational processes is arguably at the center of Artificial Life research. How might LLMs be specifically relevant to Artificial Life, as opposed to the subdiscipline (yes, that’s ironic) of AI? Here are a few ideas.The production of novelty can be explored through the use of an LM that continually takes as its input text composed by humans, other LMs, and its own output. Such a system is relevant to our field’s interests in feedback loops, open-endedness, and the emergence of complexity. Is this system engaged in language acquisition through “social” interactions?LMs might be used to explore questions related to the emergence of meaning in language. Can meaning be generated by an LM, or is it specific to living things? Can LMs evolve to be better interpreters and writers? How does the text LMs generate change the way humans produce and use language?If any work responding to these questions was presented in the form of a formal research experiment documented in an article, then this would naturally fall within the scope of human-authored research. However, an LLM-generated poem, song, or essay can be of value to researchers in Artificial Life exploring these topics (even if it isn’t very good; Cave, 2023). The coauthorship of such a work by an LM and a human as a way of communicating ideas about Artificial Life would be interesting to consider. In this case, the text contributed by the LM would need to be quoted as an “example” within the text of a submission made by a human author who determined that it was worthy of submission. Even though the text itself is a direct, self-referential, and, we would hope, revealing exploration of an LM system’s quirks, capabilities, or limitations, the determination of its relevance must, for the time being at least, be made by and attributed to a human.There is a precedent for such work, much of which has been explored under the banners of cybernetic, generative, and Artificial Life art (e.g., see many historical examples in Benthall, 1972; Ohlenschläger, 2012; Reichardt, 1968; Whitelaw, 2004). In such contexts, the art is published. Associated commentary and/or explanations may come later, and these might not be authored by the same system or person who made the original work.An interview, debate, discussion, or duet between a human and an AI, or between several computer programs, can also challenge our ideas about living systems and their exchange of information, the use of language, or the production of improvised movement and sound. The inclusion of extracts from discussions with computer chatbots dates back at least to the advent of ELIZA in the 1960s: “Men are all alike—IN WHAT WAY—They’re always bugging us about something or other” (Weizenbaum, 1966, p. 36). Likewise collaborative improvisations performed by robots, algorithms, and humans have an established place in music (Bown, 2011; Eldridge, 2005). As far as we know, the journal hasn’t published such works previously. But we could.For something along these lines to be published today, as with any contribution, it would of course need to provide novel perspective or insight. However, the main point here is that in these scenarios, even though we might intuitively feel that the generative AI system warrants the status of contributor at the level of coauthor, we have to insist on a human author of the submission. They would have ultimate responsibility for the work produced by the generative algorithm so that, for instance, if the LLM’s poetry influenced thought in a positive and productive way, or if it incited violence, we would have somebody accountable to thank or blame. If the article’s publication required the payment of an open access fee, we would also have somebody from whom to extract the payment!We haven’t yet received any submissions made by generative AI (as far as we know). But this issue contains novel work by human authors. In fact, we have recently published a spate of varied special issues reporting on the research presented at human gatherings, some in person, some online. These have covered a wide range of exciting activity in the Artificial Life community: Issue 28:2 has extended versions of selected papers from the 2019 Artificial Life conference; 28:3 is a collection of articles on embodied intelligence; 28:4 is the Artificial Life 2021 conference special issue; 29:1 explores agent-based models of human behavior. We extend our thanks to all the guest editors for their hard work in handling the selection and review of articles for their issues. New ideas for special issues in the subdomains of Artificial Life are always welcome. If you have an idea, please contact us.After that run of special issues, we welcome you back to a general issue of contributed research articles that cover a wide range of Artificial Life topics—including distributed control, emergence, dynamical systems, self-organization, game theory, artificial chemistry, and biocomputing—addressed through theory, models, simulations, and physical experiments.We start with a letter from Bull and Liu, on “A Generalised Dropout Mechanism for Distributed Systems.” They use a modified NK model to sharpen the criteria for determining when local control is more beneficial than global control. Next, we have an article from Gershenson, on “Emergence in Artificial Life.” He uses the difference in information present at different levels of a system as the basis for a new definition of emergence, one of the fundamental components of ALife.The article from Howison et al., “On the Stability and Behavioral Diversity of Single and Collective Bernoulli Balls,” describes a platform for investigating how dynamical systems may be used as the basis for designing a variety of agent behaviors. This platform, both in simulation and as a physical system, comprises a collection of “Bernoulli balls” in an airflow, interacting with each other and with the flow. The aim is to develop a dynamical system with a diverse set of possible behaviors.Ichinose et al. present “How Lévy Flights Triggered by the Presence of Defectors Affect Evolution of Cooperation in Spatial Games.” Lévy flights model a kind of random motion with both small and big displacements. Here Lévy flights are combined with game theory concepts in an agent-based model. The authors investigate how the presence of defectors changes the optimal behaviors.Next, Scott and Pitt investigate “Interdependent Self-Organizing Mechanisms for Cooperative Survival.” Complex survival games, where cooperation is needed to survive intermittent catastrophes, need complex strategies. Here the authors look at social self-organization, which, as any complex domain, has aspects that can make the situation better in some cases and, in other cases, worse. They conclude that such systems need to be able to reflect on their own operation through some kind of self-model.Sienkiewicz and Jędruch tell us about “DigiHive: Artificial Chemistry Environment for Modeling of Self-Organization Phenomena.” This two-dimensional continuous space simulation environment supports experiments with the goal of facilitating open-ended simulations. It steers more toward natural physical and biological systems (e.g., it includes energy conservation), rather than toward the more abstract operation of some other artificial chemistries. The authors describe the rationale and operation of the system and use it to investigate aspects of self-organization and self-replication in cellular-like systems.Finally, Svahn and Prokopenko examine “An Ansatz for Computational Undecidability in RNA Automata.” An ansatz is an “educated guess” about the form of the solution to a problem that can be used to provide a stepping-stone to finding the solution. Here the approach uses the known computational power of a set of automaton models as the form of solution and shows how RNA behaviors map to these models to demonstrate the computational power of this biological form of computing.

We start this issue with a research article by Keith L. Downing, “The Evolution of Conformity, Malleability, and Influence in Simulated Online Agents.” The author uses an agent-based model to investigate the potential of positive feedback in personalized recommender systems for driving a population to polarization and conformity.Following this, we include a series of articles forming a special issue from the LIFELIKE Computing Systems Workshop, held at the Artificial Life conference in Prague/Online, 2021. The workshop was co-organized by Peter Lewis, Anthony Stein, Jean Botev, and Sven Tomforde. Lewis serves here as guest editor of the special issue.We close this brief editorial to issue 29:4 by noting that our next issue will be the first of volume 30. We will be celebrating 30 years of Artificial Life by hosting several special anniversary articles giving personal perspectives on the history, development, and future of our subject. We are looking forward to publishing an amazing set of pieces.

In this issue we are pleased to share with you a diverse set of reading materials. Sadly, we mark with an obituary the passing of Julian Miller, a researcher whose loss has been keenly felt within the community of Artificial Life researchers. He shall be sorely missed.On a much brighter note, the second installment of Chris Adami’s column exploring how artificial evolution might facilitate the design of General Intelligence is to be found within the pages of this issue. Adami explains how the indirect encoding of artificial brains to facilitate neuro-evolution might be managed. He discusses approaches to choosing an appropriate neuron, how to connect neurons to create a functioning network, how to train the network, and how the different options scale up to high levels of complexity. Drawing such connections between the techniques of Artificial Life and the concerns of Artificial Intelligence is key (we feel) to enhancing the recognition that embodiment, developmental processes, and evolutionary processes all have a role to play in the emergence of natural intelligence – to overlook this whilst striving for artificial general intelligence is likely problematic.Simon Penny, an artist long engaged in Artificial Life art and robotics, provides for us a critical review of a new book by Sofian Audry, Art in the Age of Machine Learning (MIT Press 2021). The title might seem to be slightly out of line with Artificial Life’s main focus, perhaps even more suited to an AI readership, but, as Penny points out, this isn’t necessarily the case. In fact, by presenting both the practical artistic-technological concerns of the day, and the philosophical issues these raise with respect to agency, creativity and art-making by machines, Audry is in fact delving into areas that should concern us as researchers of Artificial Life.A topic infrequently explored within the pages of this journal is the impact that Artificial Life has on human relationships. In Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots, Paolo Euron enters this space by examining “physical beauty according to the artistic, cultural, and philosophical traditions”, of sexbots. Since Euron focuses on the visual appearance of these humanoid robots, with this article we have adopted a new approach for the Artificial Life journal to widen the perspective. The text is therefore supported by commentaries the editors have sought from alternative points of view. Thomas Arnold provides comment on Euron’s work from the perspective of Human-Robot Interaction by assessing the ethics of sex robots and how concepts of human trust, dignity, and autonomy potentially influence our interactions with such machines. Maria O’Sullivan examines how human interactions with sexbots relate to gender power relations and our expectations and human norms of intimacy and vulnerability. She also considers the very real dangers now widely associated with the commodification of beauty and the potential for moral harm that may result from an increase in the ubiquity or use of sexbots. We hope that you find the article and commentaries thought provoking.The issue also includes five other intriguing research articles.In Computation by Convective Logic Gates and Thermal Communication, Bartlett et al. demonstrate a simulation of an embodied computational system of Boolean NOR gates that can be realised in a convective fluid. This demonstrates that computation can be achieved in relatively simple physical contexts.The article Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution, by Benureau and Tani, flips the usual biological timescales (slow evolution, faster developmental processes) to one where morphological development is slower than evolution, and applies this in the context of robot design. Early in the evolutionary process, robots are ‘babies’, and only later on in evolution have they developed into ‘adults’. The authors investigate this novel approach to evolve diverse gaits.In Effect of Environmental Change Distribution on Artificial Life Simulations, Bullinaria explores the effect of environmental change (modelled as coloured noise) on evolutionary outcomes. He carefully dissects two factors: the average size, and the distribution of changes, to show that results are not as sensitive to noise colour as previously thought.Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks, authored by Elek et al., takes inspiration from slime mould growth to develop an algorithm for reconstructing networks from sparse data. The authors demonstrate their algorithm by applying it to a case study rarely seen in the Artificial Life arena: cosmological data sets of the large-scale distribution of gas and dark matter in the universe.In From Dynamics to Novelty: An Agent-Based Model of the Economic System, Recio et al. explicitly include time evolution in a new model of a complex self-organising economy. They realise their model in an agent-based simulation of an economy that can generate new technologies and products, and investigate the dynamics of the resulting system.We hope you enjoy the issue!

Julian Francis Miller1It is with great sadness that we report the death of our colleague and friend, Julian Miller.Julian’s work is well known throughout the Artificial Life community: His Cartesian genetic programming (CGP) and in materio computing are foundational concepts. He also made contributions in morphological computing and neurocomputing, all based on his fascination with evolution as a means of attacking and solving problems. Like many in the ALife community, he had an interdisciplinary career, commencing with a first degree in Physics and a PhD in Mathematics, followed by research in Natural Computing and material computing at the universities of Napier, Birmingham, and York in the UK.Julian invented CGP (Miller, 1999), a way of encoding graph programs (functional nodes connected by edges) in a string of integers, allowing the string to be evolved in the standard way, with the graph (located on a Cartesian grid, hence its name) produced as the result of a genotype to phenotype mapping. From this simple beginning, Julian and his students continued to develop the approach, and other researchers joined in. Ten years later, the field had grown significantly, with many researchers both using CGP in their own work and extending the original concept. Indeed, the field had grown enough that Julian could edit an entire book on the topic (Miller, 2011). Ten years later still, the field shows no signs of abating, and Julian wrote a 40-page review for Genetic Programming and Evolvable Machines on CGP’s status, its many variants, and its future prospects (Miller, 2020).Julian was also a pioneer in the field of in materio computing (Miller & Downing, 2002), which exploits the physical properties of unconventional materials, such as liquid crystals (Harding & Miller, 2004) and carbon nanotubes (Miller et al., 2014), to perform computation intrinsically, in what he dubbed a “Field Programmable Matter Array.” His original work used evolutionary algorithms directly to configure the materials. Later, he also used Reservoir Computing as a more abstract model for getting these materials to compute (Dale et al., 2017). This is another field with explosive growth, so much so that some authors have even published on the name of the domain itself (Ricciardi & Milano, 2022). Julian was there from the start, contributing his insights and ideas throughout.CGP and in materio computing may be what Julian is best known for, but these contributions were embedded in a deeper research program of understanding development as a fundamental component of evolving embodied computation. From growing a self-repairing “French-flag” organism (Miller, 2004), to assembling complex structures through Artificial Chemistries (Faulconbridge et al., 2011), to the idea of the “software garden” (Miller, 2018), Julian felt that both growth and evolution are essential concepts in complex systems.His Festschrift Inspired by Nature (Stepney & Adamatzky, 2018) was a (slightly late) 60th birthday present from his many academic colleagues. It includes chapters contributed by a wide range of authors who have built on and been inspired by his many research interests. The text covers evolution and hardware, CGP applications, chemistry, and development. Julian retired in 2016, but he did not stop his research. He used the freedom from the quotidian constraints of an academic job to pursue a new interest. He was bringing together his discoveries in evolution, development, networks, and computation to develop a new neural model to evolve programs that build, or grow, neural networks. His most recent publication on that topic has only just appeared (Miller, 2022).Julian’s retirement also allowed him to spend time with his recently acquired beloved new family. His wife Gabi remembers him thus: “He was a loving and generous-hearted husband, a wise step-father to my three grown adults and much loved Grandpa to our four grand-children. Jules will be sadly missed, but also lovingly remembered by all whose life he touched.” Many further tributes to Julian from his colleagues can be found in the latest SIGEVO newsletter (Ochoa, 2022).

OBJECTIVE: Integration of environmentally sustainable digital health interventions requires robust evaluation of their carbon emission life-cycle before implementation in healthcare. This scoping review surveys the evidence on available environmental assessment frameworks, methods, and tools to evaluate the carbon footprint of digital health interventions for environmentally sustainable healthcare. MATERIALS AND METHODS: Medline (Ovid), Embase (Ovid). PsycINFO (Ovid), CINAHL, Web of Science, Scopus (which indexes IEEE Xplore, Springer Lecture Notes in Computer Science and ACM databases), Compendex, and Inspec databases were searched with no time or language constraints. The Systematic Reviews and Meta-analyses Extension for Scoping Reviews (PRISMA_SCR), Joanna Briggs Scoping Review Framework, and template for intervention description and replication (TiDiER) checklist were used to structure and report the findings. RESULTS: From 3299 studies screened, data was extracted from 13 full-text studies. No standardised methods or validated tools were identified to systematically determine the environmental sustainability of a digital health intervention over its full life-cycle from conception to realisation. Most studies (n = 8) adapted publicly available carbon calculators to estimate telehealth travel-related emissions. Others adapted these tools to examine the environmental impact of electronic health records (n = 2), e-prescriptions and e-referrals (n = 1), and robotic surgery (n = 1). One study explored optimising the information system electricity consumption of telemedicine. No validated systems-based approach to evaluation and validation of digital health interventions could be identified. CONCLUSION: There is a need to develop standardised, validated methods and tools for healthcare environments to assist stakeholders to make informed decisions about reduction of carbon emissions from digital health interventions.

November 02 2021 Editorial: News from the New Co-Editors in Chief Alan Dorin, Alan Dorin Monash University, Computational and Collective Intelligence Group, Department of Data Science and AI, Faculty of Information Technology. [email protected] Search for other works by this author on: This Site Google Scholar Susan Stepney Susan Stepney University of York, Department of Computer Science. [email protected] Search for other works by this author on: This Site Google Scholar Author and Article Information Alan Dorin Monash University, Computational and Collective Intelligence Group, Department of Data Science and AI, Faculty of Information Technology. [email protected] Susan Stepney University of York, Department of Computer Science. [email protected] Online Issn: 1530-9185 Print Issn: 1064-5462 © 2021 Massachusetts Institute of Technology2021Massachusetts Institute of Technology Artificial Life (2021) 27 (2): 73–74. https://doi.org/10.1162/artl_e_00350 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Search Site Citation Alan Dorin, Susan Stepney; Editorial: News from the New Co-Editors in Chief. Artif Life 2021; 27 (2): 73–74. doi: https://doi.org/10.1162/artl_e_00350 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentAll JournalsArtificial Life Search Advanced Search 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. Mark also led ISAL (International Society for Artificial Life) during the years 2001–2015. In the mid-2000s, he was also Chief Operating Officer of ProtoLife SRL... © 2021 Massachusetts Institute of Technology2021Massachusetts Institute of Technology You do not currently have access to this content.

When learning sign language, feedback on accuracy is critical to vocabulary acquisition. When designing technologies to provide such visual feedback, we need to research effective ways to identify errors and present meaningful and effective feedback to learners. Motion capture technologies provide new opportunities to enhance sign language learning experiences through the presentation of visual feedback that indicates the accuracy of the signs made by learners. We designed, developed, and evaluated an embodied agent-based system for learning the location and gross motor movements of sign language vocabulary. The system presents a sign, tracks the learner's attempts at a sign, and provides visual feedback to the learner on their errors. We compared five different types of visual feedback, and in a study involving 51 participants we established that learners preferred visual feedback where their attempts at a sign were shown concurrently with the movements of the instructor with or without explicit corrections.

Artists and engineers have devised lifelike technology for millennia. Their ingenious devices have often prompted inquiry into our preferences, prejudices, and beliefs about living systems, especially regarding their origins, status, constitution, and behavior. A recurring fabrication technique is shared across artificial life art, science, and engineering. This involves aggregating representations or re-creations of familiar biological parts-techno-hybridization-but the motives of practitioners may differ markedly. This article, and the special issue it introduces, explores how ground familiar to contemporary artificial life science and engineering has been assessed and interpreted in parallel by (a) artists and (b) theorists studying creativity explicitly. This activity offers thoughtful, alternative perspectives on artificial life science and engineering, highlighting and sometimes undermining the fields' underlying assumptions, or exposing avenues that are yet to be explored outside of art. Additionally, art has the potential to engage the general public, supporting and exploring the findings of scientific research and engineering. This adds considerably to the maturity of a culture tackling the issues the discipline of artificial life raises.

We discuss approaches to agent-based model visualization. Agent-based modeling has its own requirements for visualization, some shared with other forms of simulation software, and some unique to this approach. In particular, agent-based models are typified by complexity, dynamism, nonequilibrium and transient behavior, heterogeneity, and a researcher's interest in both individual- and aggregate-level behavior. These are all traits requiring careful consideration in the design, experimentation, and communication of results. In the case of all but final communication for dissemination, researchers may not make their visualizations public. Hence, the knowledge of how to visualize during these earlier stages is unavailable to the research community in a readily accessible form. Here we explore means by which all phases of agent-based modeling can benefit from visualization, and we provide examples from the available literature and online sources to illustrate key stages and techniques.

We present data demonstrating that the application of interactive evolution and related techniques has been growing since the early 1990s. Much research has honed the technique for specific applications. In this paper, we explicitly consider the interaction between chance and human creative tendencies as exercised by manual selection during interactive evolutionary computation. Since stochastic processes have interacted with dynamical human and technological processes for creative design throughout the history of art, we survey a few pertinent examples as we tackle interactive evolutionary computing specifically. In this context, chance governs the crossover and mutation of genes and therefore ultimately decides which forms will be displayed to a user for consideration. We derive some simple suggestions as to how chance's role may be extended in interactive evolution, demonstrate these in practice, and discuss how such randomness benefits human creativity.

We use a new measure of creativity as a guide in an interactive evolutionary art task and tie the results to natural language usage of the term “creative.” Following previous work, we explore a tractable definition of creativity, one emphasizing the novelty of systems, and its addition to an interactive application. We next introduce a generative ecosystemic art system, EvoEco, an agent-based pixel-level means of generating images. EvoEco is used as a component of an online survey which asks users to evolve a pleasing image and then rank the success of the process and its output. Evolutionary search is augmented with the creativity measure, and compared with control groups augmented with either random search or a measure of phenotypic distance. We show that users consistently rate the creativity measure-enhanced version as more “creative” and “novel” than other search techniques. We further derive additional insights into appropriate forms of genetic representation and pattern space-traversal in an interactive evolutionary algorithm.

In this paper, we explore a generative art system designed to promote the creation of a diverse range of aesthetically pleasing images. We introduce our system, EvoEco, an agent-based pixel-level means of generating images based on artificial ecosystems. This art system is driven by interactive evolutionary computation, and further augmented using special measures to promote the diversity of the individuals. Following previous work, we explore a tractable definition of creativity and its addition to this interactive search. EvoEco was released online, and used by forty-one anonymous users to generate artwork. Here we present some of the discovered results.

In this paper, we introduce a new image database, consisting of examples of artists' work. Successful classification of this database suggests the capacity to automatically recognize an artist's aesthetic style. We utilize the notion of Transform-based Evolvable Features as a means of evolving features on the space, these features are then evaluated through a standard classifier. We obtain recognition rates for our six artistic styles - relative to images by the other artists and images randomly downloaded from a search engine - of a mean true positive rate of 0.946 and a mean false positive rate of 0.017. Distance metrics designed to indicate the similarity between an arbitrary greyscale image and one of the artistic styles are created from the evolved features. These metrics are capable of ranking control images so that artist-drawn instances appear at the front of the list. We provide evidence that other images ranked as similar by the metric correspond to naïve human notions of similarity as well, suggesting the distance metric could serve as a content-based aesthetic recommender.

October 01 2006 Metacreation: Art and Artificial Life. Mitchell Whitelaw. (2004, MIT Press.) 281 pages. Alan Dorin Alan Dorin Search for other works by this author on: This Site Google Scholar Author and Article Information Alan Dorin Online Issn: 1530-9185 Print Issn: 1064-5462 © 2006 Massachusetts Institute of Technology2006 Artificial Life (2006) 12 (4): 635–637. https://doi.org/10.1162/artl.2006.12.4.635 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Alan Dorin; Metacreation: Art and Artificial Life. Mitchell Whitelaw. (2004, MIT Press.) 281 pages.. Artif Life 2006; 12 (4): 635–637. doi: https://doi.org/10.1162/artl.2006.12.4.635 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2006 Massachusetts Institute of Technology2006 Article PDF first page preview Close Modal You do not currently have access to this content.

Playthings are often engineered to replicate the character of real organisms. In the past, inventors lavished great expense on their lifelike automata, their constraints being typically related to the mechanical technology they employed and the amount of time and effort they were able to commit to the enterprise. The devices that are currently produced are usually intended for the mass market. The cost of production therefore is a major concern, even though the technology is more sophisticated and highly automated than in the past. Consequently, toymakers and engineers, as well as artists, of the past and present alike have had to think abstractly about living systems in order to construct their simulacra economically. This essay examines a number of lifelike toys to discover the properties of real organisms that their designers have attempted to recreate. That we, as users of these devices, so readily recognize in them a degree of lifelikeness demonstrates the extent to which intuition may sway our intellectual reasoning about real biology. As a result, an innovative toymaker or artist is able to manipulate us to zoomorphize even the most extreme abstractions--at least momentarily--despite our rational reluctance to accept the trickery.

The views of some artists on what constitutes life are explored, with the aim of challenging those within the artificial life research community to rethink and perhaps expand their own views about the term and its meaningful application. The focus is on the musical works of Steve Reich and the paintings of Wassily Kandinsky. The role of the observer in determining when it is appropriate to label a thing as living is also discussed.