Special Issue on Lifelike Computing Systems

Technological systems have been a part of human life since prehistory. Although they initially took the form of passive tools, such as axes and spoons, the Industrial Revolution saw the advent of powered, mechanized technology, operating “under it’s own steam,” without direct human control over every action. By integrating more complex information processing machinery, automation evolved into autonomy as decision-making and self-regulation became features of modern technology. Now, so-called intelligent systems, embodying techniques from the field of artificial intelligence (AI), are designed with the explicit intention of replicating rational behaviors and the sorts of things that minds do, inside technological systems.At the same time, the study of Artificial Life (ALife) (Langton, 1987) has explored the properties of living systems, both as they are found in nature, as they might be, and as humans can build them. This has exposed a large variety of mechanisms that produce qualities typically associated with life. Examples include self-organization, homeostasis, self-replication, evolution, learning, self-awareness, and many others besides.The Lifelike Computing Systems initiative (Stein et al., 2021b) aims to learn from the study of life and living systems to develop new, useful, “lifelike” systems; a further aim is to identify when such features are of value. The focus of this research direction is primarily on engineered technological systems broadly within the domain of computing.The notion of “lifelike computing” is not intended to separate itself from or replace previous initiatives; in a large number of cases, there are already technologies and research efforts that strongly lean toward lifelike computing systems in specific aspects. Building on a long and highly successful tradition in biologically inspired computing, the “lifelike” vision not only seeks inspiration in the living world but also seeks to replicate its qualities explicitly in technological systems. Indeed, we cannot claim that all bio-inspired systems remain lifelike, nor is this in general even always a desirable outcome for those designing bio-inspired systems. The agenda also goes beyond fundamental ALife research, often rightly exploratory in nature, because it focuses explicitly on building purposeful and reliable technological systems for people, based on ALife principles. Therefore the vision of explicit replication of lifelike qualities in technological systems of value to humanity marks a sharpening of focus.This special issue is a follow-up to the workshop series “Lifelike Computing Systems,” held at the International Conference on Artificial Life in 2020 and 2021 (Stein et al., 2021a), and again in 2022 (Stein et al., 2023). The workshop series hosted diverse talks showcasing early-stage research and work in progress, with topics ranging from plasticity in technical systems to artificial DNA, from self-explaining systems to realistic humanoid and animal robots.We have therefore solicited papers that explore and contribute to the discussion on research questions we deem key to be further explored: Which qualities of life are of high relevance and benefit for the engineering of lifelike computing systems useful to people? Why? How?How can we integrate and combine insights and methodological approaches from existing, related research initiatives, such as cybernetics, self-aware computing, organic computing, and autonomic computing?Which methods from domains like artificial life, bio-inspired computing, artificial intelligence, and self-adaptive and self-organizing systems contribute to achieving lifelike features of computing systems?When is more “lifelike” technology appropriate? What are the challenges associated with embedding technology that is more “lifelike” in society? How can these be tackled?This special issue represents an opportunity for more mature work emerging from this line of research to be presented. It contains four papers that together provide a review, analysis, and critique of the integration of lifelike properties into engineered systems, in many cases proposing concrete recommendations for future research directions and methods.In “Lessons from the Evolutionary Computation Bestiary,” Campelo and Aranha explore and critique the explosion of metaphor-centered metaheuristic methods that have been published in recent years and that claim to be inspired by—in their view—increasingly absurd natural phenomena. Examples surveyed include several different types of birds, mammals, fish, and invertebrates; soccer and volleyball; and even reincarnation, zombies, and gods. The authors acknowledge that metaphors can be powerful inspiration and explanatory tools and that, indeed, the field of metaheuristics has a long history of finding inspiration in natural systems, starting from evolution strategies, genetic algorithms, and ant colony optimization. However, they question the value of the emergence of hundreds of highly similar variants of essentially the same algorithm under different labels. The authors have curated a “bestiary” of such variants over the years, and their article in this issue reviews this, arguing that this proliferation has been counterproductive to scientific progress in the field. They argue that it does little to improve our ability to understand and simulate biological systems and that it can actively impede an improved understanding of how to design and analyze global optimization techniques. The article discusses why this social phenomenon in research may have occurred in recent years and its negative consequences, ending with a call to improve the scientific soundness of metaheuristic research.In “Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?,” Tzanetos asks whether all nature-inspired algorithms remain lifelike. The article considers the history of evolutionary computation and, as in the first article, the proliferation of many so-called nature-inspired techniques in recent years. The author juxtaposes the value of such techniques in solving hard problems with an analysis to support an argument that the mathematics of these techniques often does not match the source behavior faithfully. In these cases, can it be said that the algorithms are indeed “lifelike,” and if not, does that matter, so long as they provide value in terms of their ability to solve problems intelligently? The article argues that historically, there was greater alignment between the algorithmic models and source behaviors, but this is often not seen in more recent attempts. The article ends by discussing if there is a need for new lifelike features of algorithms, concluding that this is not helpful—instead presenting recommendations for future research in nature-inspired computing, which, the authors argue, would move the field in “the right direction.”In “Assessing Model Requirements for Explainable AI: A Template and Exemplary Case Study,” Heider et al. explore the explainability of decision support systems that use evolutionary rule-based machine learning techniques, more precisely, learning classifier systems (LCSs). Self-adaptive and self-optimizing systems are necessarily dynamic, yet for them to be accepted by people in sociotechnical settings, explanations for machine-made decisions are often essential. The authors argue that rule-based machine learning models, such as LCSs, present an opportunity for transparent machine learning models that naturally support access to explanations. To assist with designing and evaluating such models, they also propose a generic and thus broadly applicable questionnaire template. The template is demonstrated to provide valuable insights for the design of such LCS models in specific scenarios. The approach is illustrated in a manufacturing case study.Finally, in “Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives,” Casadei surveys computational techniques based on or harnessing “collectiveness,” often seen in many living systems, to produce capabilities beyond what can be achieved with individual or monolithic systems. A key concept common to these techniques is that such systems can exploit a large number of individuals to produce intelligent collective behavior out of not-so-intelligent components. The article argues that there is a trend in some areas of engineering toward this way of designing technological systems, citing examples such as the Internet of Things, swarm robotics, and crowd computing and emphasizing that these technologies span many techniques, systems, and application areas. An essential finding of the review is that there is substantial fragmentation of this research, however, and that the so-called “verticality” of research communities makes a common fundamental understanding of such systems challenging to achieve. The author argues that an important challenge is identifying, placing in a common structure, and ultimately connecting the different areas and methods addressing intelligent collectives. As such, the article presents a set of questions aimed at mapping out collective intelligence research. It uses this to develop a set of preliminary notions, concepts, and perspectives, as well as associated research opportunities, to develop a more fundamental understanding of computational collective intelligence engineering.The guest editors thank the authors of papers submitted to the “Lifelike Computing Systems” special issue as well as the reviewers, who gave valuable feedback to all the authors. We would also like to thank the organizers of the ALife conferences that hosted the Lifelike Computing Systems workshops as well as all the speakers and participants who contributed to many vibrant debates that informed the direction of the final set of articles in this issue. Last, we thank the Board of Editors of Artificial Life for supporting this special issue.