Artificial Life Systems Research Articles

Aggregate Boid behavior to aid in artificial autopoietic organization

Analyzing carbon-based life on earth can lead to biased inferences on the nature of life as might exist in elsewhere in the universe in alternative forms, therefore, scientists have looked into either abstracting life into constituent systems it is comprised of, or logics of life, or lists of essential criteria, or essential dynamic patterning that characterizes the living. A system-level characterization that is and referred to as a general pattern of minimal life is autopoiesis (Varela et al., 1974) including production, maintenance and replacement of required constituents for setting up and maintaining an internal environment with self/other separation that regulates and is constitutive of processes that produce the environment and components for processes that comprise this ongoing activity of self-production in ‘recursively’, i.e., in a manner that allows the organizational pattern to continually reconstitute the conditions, components and processes required for its own perpetuation. This seminal concept of an autopoiesis is instantiated in life as we know it, but might also be instantiated in different media and in unforeseen ways. Other researchers have argued life is more than autopoiesis and that it is a co-emergent property of autopoiesis and cognition. Life produces many emergent properties such as synchronization and patterns as seen in flocks and herds of different animal species. The mechanics of this synchrony displayed in flocks and herd animals has been extracted by Craig Reynolds into a generative model referred to as “Boids”. With these concepts in mind, we address the following research question: How can the synchronous maneuvers and aggregate behavior of Boids contribute to constitutive subsystems in realizing an autopoietic system? Can such a system exhibit minimal cognition? This work attempts to answer these questions with a bottom-up approach to constructing an artificial life system. We exhibit a computational model of autopoiesis and a minimal level of cognition in the sense of M. Bitbol and P. Luigi Luisi, whereby an autopoietic entity engages in active assimilation of external components as part of its activity of self-production.

Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity

Genetic programming and artificial life systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming and artificial life systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.

On the Open-Endedness of Detecting Open-Endedness.

We argue that attempting to quantify open-endedness misses the point: The nature of open-endedness is such that an open-ended system will eventually move outside its current model of behavior, and hence outside any measure based on that model. This presents a challenge for analyzing Artificial Life systems, leading us to conclude that the focus should be on understanding the mechanisms underlying open-endedness, not simply on attempting to quantify it. To demonstrate this, we apply several measures to eight long experimental runs of the spatial version of the Stringmol automata chemistry. These experiments were originally designed to examine the hypothesis that spatial structure provides a defense against parasites. The runs successfully show this defense, but also show a range of innovative, and possibly open-ended, behaviors involved in countering a parasitic arms race. Commencing with system-generic measures, we develop and use a variety of measures dedicated to analyzing some of these innovations. We argue that a process of analysis, starting with system-generic measures but going on to system-specific measures, will be needed wherever the phenomenon of open-endedness is involved.

The perceived effects of augmented trail sensing and mood recognition abilities in a human–fish biohybrid system

The use of technologies to enhance human and animal perception has been explored in pioneering research about artificial life and biohybrid systems. These attempts have revealed that augmented sensing abilities can emerge from new interactions between individuals within or across species. Nevertheless, the diverse effects of different augmented capabilities have been less examined and compared. In this work, we built a human–fish biohybrid system that enhanced the vision of the ornamental fish by projecting human participants onto the arena background. In contrast, human participants were equipped with a mixed-reality device, which visualized individual fish trails (representing situation-oriented perceptions) and emotions (representing communication-oriented perceptions). We investigated the impacts of the two enhanced perceptions on the human side and documented the perceived effects from three aspects. First, both augmented perceptions considerably increase participants’ attention toward ornamental fish, and the impact of emotion recognition is more potent than trail sense. Secondly, the frequency of human–fish interactions increases with the equipped perceptions. The mood recognition ability on the human side can indirectly promote the recorded positive mood of fish. Thirdly, most participants mentioned that they felt closer to those fish which had mood recognition ability, even if we added some mistakes in the accuracy of mood recognition. In contrast, the addition of trail sensing ability does not lead to a similar effect on the mental bond. These findings reveal several aspects of different perceived effects between the enhancements of communication-oriented and situation-oriented perceptions.

An Acoustic Artificial Life System Using the Game of Life and its Application for Performing Arts

An Acoustic Artificial Life System Using the Game of Life and its Application for Performing Arts

Life-Inspired Endogenous Dynamic Behavior of Lipid Droplet-like Microcompartments in Artificial Adipocyte-like Structures

Chemical systems that can replicate cellular behaviors are gaining increasing attention and are being used to study various biological processes. Here, a protein- or amylose-based assembly at an oi...

Maximum Individual Complexity is Indefinitely Scalable in Geb.

Geb was the first artificial life system to be classified as exhibiting open-ended evolutionary dynamics according to Bedau and Packard's evolutionary activity measures and is the only one to have been classified as such according to the enhanced version of that classification scheme. Its evolution is driven by biotic selection, that is (approximately), by natural selection rather than artificial selection. Whether or not Geb can generate an indefinite increase in maximum individual complexity is evaluated here by scaling two parameters: world length (which bounds population size) and the maximum number of neurons per individual. Maximum individual complexity is found to be asymptotically bounded when scaling either parameter alone. However, maximum individual complexity is found to be indefinitely scalable, to the extent evaluated so far (with run times in years and billions of reproductions per run), when scaling both world length and the maximum number of neurons per individual together. Further, maximum individual complexity is shown to scale logarithmically with (the lower of) maximum population size and maximum number of neurons per individual. This raises interesting questions and lines of thought about the feasibility of achieving complex results within open-ended evolutionary systems and how to improve on this order of complexity growth.

The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems.

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure a system's ability to produce commonly-agreed-upon hallmarks of open-ended evolution: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide detailed algorithms (including C++ implementations) for these metrics that should be easy to incorporate into existing artificial life systems. Furthermore, we expect this toolbox to continue to grow as researchers implement these metrics in new languages and as the community reaches consensus about additional hallmarks of open-ended evolution. For example, we would welcome a measurement of a system's potential to produce major transitions in individuality. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK landscapes and the Avida digital evolution platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

Android Corporeality, Transhuman Performance and the Japanese ‘Mind’

This essay examines Alter, an android that was developed collaboratively by Japanese roboticist Hiroshi Ishiguro and A-Life (artificial life) scientist Takashi Ikegami. Alter is an experimental robot platform conceived as a ‘machine human’. Alter was created at the juncture of Ishiguro’s interest in android appearance and Ikegami’s search for what he sees as internal principles underlying life. While Ishiguro’s anthropomorphic machines are intended as models for future humanoid robots in workplaces and domestic contexts, they are also intended as experimental tools for his investigations on human identity, the ‘soul’, and sociality, and related philosophical questions, such as ‘What is the human?’ In contrast, Ikegami works with a different question: ‘What is natural life?’ Ikegami’s question emerges from his research interest in and development of artificial life systems, which combine computer simulation, chemical experiments, and robotics. Alter, presented as an interactive performance installation at a science museum in Japan in 2016, was intended as a model with which to study human embodiment and sentience. In this essay, I examine how Alter’s 2016 performance was designed dramaturgically to induce a sense of agency hovering between the organic and the inorganic, the natural and mechanical, the random and controlled, and between the autonomous and programmed. I discuss how Alter rehearsed human-machine inter-corporeality, and how its dramaturgical staging contributed to its agency.

High-performance computing framework with desynchronized information propagation for large-scale simulations

The parallel implementation of complex, often micro-scale simulation systems such as social, artificial life, or traffic systems poses a significant challenge for scientists and often requires the use of supercomputer devices. At the same time, it is quite difficult to develop a software system capable of being scaled up to hundreds or thousands of nodes due to the inherent algorithmic problems hampering efficiency (such as the need to propagate information about the state of the environment—in other words, the inevitable need to have a means of state synchronization). In this paper, we propose a framework based on a desynchronized method for the distribution of information inspired by the propagation of smell. It does not inhibit the overall scalability and efficiency, making use of available HPC resources. The implementation presented here leverages an actor model for parallelization as well as Akka Cluster distribution mechanisms for cluster management, providing a seamless tool-chain for large-scale simulations following realistic rules found in nature. As an example, three real world-inspired simulations are presented and tested, proving a linear scalability of up to 3456 computing cores and correctness with a growing degree of distribution.

Towards a first implementation of the WLIMES approach in living system studies advancing the diagnostics and therapy in augmented personalized medicine

The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation, man-machine interface and creative design. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life.

Microbial Nanoids: Electronic Arts in the Face of Mexico’s Megadiversity Crisis

In this paper the authors describe the pioneering robot designed by Arcangel Constantini, called the Nanodrizas (2006). Nanodrizas is a cluster of small robots resembling extraterrestrial flying objects. They are introduced into an ecosystem for water recycling, among other effects. The result of a seriocomic robotic device like the Nanodrizas is to make the viewer aware of the specific technology that would one day allow humans to invite machines and artificial life-systems to save an environment that is precariously balanced on the edge.

EXPLORATION OF STYLIZED FACTS IN THE ARTIFICIAL LIFE SYSTEM AVIDA

Population dynamics in the evolution, extinction, and re-evolution of various logic-function performing organisms is studied in the artificial life system, Avida. Following the work of Yedid (2009), we design an experiment involving two extinction regimes, pulse-extinction (corresponding to a random-kill event) and press-extinction (corresponding to a prolonged episode of rare resources). In addition, we study the effect of environmental topology (toroidal grid and clique graph). In the study of population dynamics, logarithmic returns are generally applied. The resulting distributions display a fat tail form of the power law: the more complex the logic function (in terms of NAND components), the broader the full width at half a maximum of the histogram. The power law exponents were in sound agreement with those of “real-life” populations and distributions. The distributions of evolutionary times, as well as post-extinction recovery periods, were very broad, and presumably had no standard deviations. Using 100 runs of 200,000 updates for each of the four cases (about 1 month of central processing unit time), we established the dynamics of the average population, with the effect of world topology.

Generalized picture distance measure and applications to picture fuzzy clustering

Picture fuzzy set (PFS), which is a generalization of traditional fuzzy set and intuitionistic fuzzy set, shows great promises of better adaptation to many practical problems in pattern recognition, artificial life, robotic, expert and knowledge-based systems than existing types of fuzzy sets. An emerging research trend in PFS is development of clustering algorithms which can exploit and investigate hidden knowledge from a mass of datasets. Distance measure is one of the most important tools in clustering that determine the degree of relationship between two objects. In this paper, we propose a generalized picture distance measure and integrate it to a novel hierarchical picture fuzzy clustering method called Hierarchical Picture Clustering (HPC). Experimental results show that the clustering quality of the proposed algorithm is better than those of the relevant ones.

酵母を用いた合成生物学 ～新しい人工生命システム構築への挑戦～

酵母を用いた合成生物学 ～新しい人工生命システム構築への挑戦～

REVISITING OVERLOOKED FOUNDATIONS OF EVOLUTIONARY COMPUTATION: PART II

Evolutionary computation is a rapidly expanding field of research with a long history. Much of that history remains unknown to most practitioners and researchers. This two-part article offers a review of selected foundational efforts in evolutionary computation, with a focus on those that have not received commensurate attention. Part I presented an initial overview of the essential components of evolutionary algorithms followed by a review of early research in artificial life and modeling genetic systems. Here, Part II reviews seminal results in evolving programs and evolvable hardware. Comments on theoretical developments and future developments conclude Part II.

Pherographia: Drawing by Ants

This paper addresses the hypothetical relationship of photography and so-called pheromone maps created by an artificial life system that simulates an ant colony and causes its activity to evolve based on the contours of images. Pheromone—used by ants to communicate via the environment—is also simulated, and from the communication and interaction of the swarm with the environment (an image) there results a kind of drawing made with the simulated pheromone. Since ants are able to detect the edges of the image, the outcome is a sketch that resembles the original image, as with old camera obscura drawings. This text explores the observable traits shared by the photographic process and the swarm's pheromone maps. The theme is discussed in the context of the emergent artificial art research field; recent theoretical advances that link swarm intelligence and cognitive sciences are also addressed.

Spatial pattern growth and emergent animat segregation

Spatial agent models can be used to explore self-organizing effects such as pattern growth and segregation. An approximate time line of key animat ideas and agent systems is presented and discussed. These ideas have led to a unique animat simulation model for studying emergence effects in artificial life systems and this predator-prey model is employed to study emergent behaviours in systems of up to around one million individual animat agents. The patterns, structures and emergent properties of the model are compared with the spatial patterns formed in non-intelligence based models that are governed only by statistical mechanics. An emergent species separation effect is found amongst the prey animats when a simple genetic marker is employed to track animats and introduce a microscopic breeding preference. Results are presented using quantitative metrics such as the animal spatial density and the pair-wise density-density correlation function. Ways in which these metrics can be used to categorize different self-organizational model regimes are discussed.

Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N), Phosphor (P) and Potassium (K). The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied.

DRIMA: A Minimal System for Probing the Dynamics of Change in a Reactive Multi-Agent Setting

DRIMA is a simple cellular model for the multi-agent setting in which reactive agents have their behavior changed by the behavior of others, as the outcome of their interactions; it is also the system that implements the model in Mathematica. It was conceived as a metaphor for the high-level issue of how agents “attract” others toward them, be it in the form of a change in any behavioral or conceptual orientation, habit, thinking, etc. This is modeled through a single behavior of the agents, which is their movement on a two-dimensional grid; as they move, they undergo interactions with each other that modify the way they move before and after the interaction. The focus of the model is on addressing issues related to the emergent dynamics of a particular setting, much in tune with an artificial life or complex systems perspective. DRIMA is purposefully meant to be simple and non-general, a minimal system for the kind of question it is designed to help address. The article is a presentation of the model and of key aspects of its implementation, not a discussion on its use to address any particular question.