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Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (02): 149–153.
Published: 01 May 2018
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Music is a peculiar human behavior, yet we still know little as to why and how music emerged. For centuries, the study of music has been the sole prerogative of the humanities. Lately, however, music is being increasingly investigated by psychologists, neuroscientists, biologists, and computer scientists. One approach to studying the origins of music is to empirically test hypotheses about the mechanisms behind this structured behavior. Recent lab experiments show how musical rhythm and melody can emerge via the process of cultural transmission. In particular, Lumaca and Baggio (2017) tested the emergence of a sound system at the boundary between music and language. In this study, participants were given random pairs of signal-meanings; when participants negotiated their meaning and played a “game of telephone” with them, these pairs became more structured and systematic. Over time, the small biases introduced in each artificial transmission step accumulated, displaying quantitative trends, including the emergence, over the course of artificial human generations, of features resembling properties of language and music. In this Note, we highlight the importance of Lumaca and Baggio's experiment, place it in the broader literature on the evolution of language and music, and suggest refinements for future experiments. We conclude that, while psychological evidence for the emergence of proto-musical features is accumulating, complementary work is needed: Mathematical modeling and computer simulations should be used to test the internal consistency of experimentally generated hypotheses and to make new predictions.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (02): 154–156.
Published: 01 May 2018
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In their commentary on our work, Ravignani and Verhoef (2018) raise concerns about two methodological aspects of our experimental paradigm (the signaling game): (1) the use of melodic signals of fixed length and duration, and (2) the fact that signals are endowed with meaning. They argue that music is hardly a semantic system and that our methodological choices may limit the capacity of our paradigm to shed light on the emergence and evolution of a number of putative musical universals. We reply that musical systems are semantic systems and that the aim of our research is not to study musical universals as such, but to compare more closely the kinds of principles that organize meaning and structure in linguistic and musical systems.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (02): 119–127.
Published: 01 May 2018
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What is the influence of short-term memory enhancement on the emergence of grammatical agreement systems in multi-agent language games? Agreement systems suppose that at least two words share some features with each other, such as gender, number, or case. Previous work, within the multi-agent language-game framework, has recently proposed models stressing the hypothesis that the emergence of a grammatical agreement system arises from the minimization of semantic ambiguity. On the other hand, neurobiological evidence argues for the hypothesis that language evolution has mainly related to an increasing of short-term memory capacity, which has allowed the online manipulation of words and meanings participating particularly in grammatical agreement systems. Here, the main aim is to propose a multi-agent language game for the emergence of a grammatical agreement system, under measurable long-range relations depending on the short-term memory capacity. Computer simulations, based on a parameter that measures the amount of short-term memory capacity, suggest that agreement marker systems arise in a population of agents equipped at least with a critical short-term memory capacity.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (02): 128–148.
Published: 01 May 2018
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Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or “soft,” aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (02): 106–118.
Published: 01 May 2018
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Life and other dissipative structures involve nonlinear dynamics that are not amenable to conventional analysis. Advances are being made in theory, modeling, and simulation techniques, but we do not have general principles for designing, controlling, stabilizing, or eliminating these systems. There is thus a need for tools that can transform high-level descriptions of these systems into useful guidance for their modification and design. In this article we introduce new methods for quantifying the viability of dissipative structures. We then present an information-theoretical approach for evaluating the quality of viability indicators , measurable quantities that covary with, and thus can be used to predict or influence, a system's viability.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (02): 85–105.
Published: 01 May 2018
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Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single-cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has not been fully explored. Here we aim at revealing a potential role of criticality of GRNs in morphogenesis, which is hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical intracellular GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogeneous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings correspond to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (1): 49–55.
Published: 01 February 2018
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This is a report on the Biological Foundations of Enactivism Workshop, which was held as part of Artificial Life XV. The workshop aimed to revisit enactivism's contributions to biology and to revitalize the discussion of autonomy with the goal of grounding it in quantitative definitions based in observable phenomena. This report summarizes some of the important issues addressed in the workshop's talks and discussions, which include how to identify emergent individuals out of an environmental background, what the roles of autonomy and normativity are in biological theory, how new autonomous agents can spontaneously emerge at the origins of life, and what science can say about subjective experience.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (1): 5–9.
Published: 01 February 2018
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We describe the questions and discussions raised at the First Workshop on Social Learning and Cultural Evolution held at theArtificial Life Conference 2016 in Cancún, Mexico in July 2016. The purpose of the workshop was to assemble artificial life researchers interested in social learning and cultural evolution into one group so that we could focus on recent work and interesting open questions. Our discussion related to both the mechanisms of social learning and cultural evolution and the consequences and influence of social learning and cultural evolution on living systems. We present the contributions of our workshop presenters and conclude with a discussion of the more important open questions in this area.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (1): 56–70.
Published: 01 February 2018
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Is undecidability a requirement for open-ended evolution (OEE)? Using methods derived from algorithmic complexity theory, we propose robust computational definitions of open-ended evolution and the adaptability of computable dynamical systems. Within this framework, we show that decidability imposes absolute limits on the stable growth of complexity in computable dynamical systems. Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems. Complexity is assessed in terms of three measures: sophistication, coarse sophistication, and busy beaver logical depth. These three complexity measures assign low complexity values to random (incompressible) objects. As time grows, the stated complexity measures allow for the existence of complex states during the evolution of a computable dynamical system. We show, however, that finding these states involves undecidable computations. We conjecture that for similar complexity measures that assign low complexity values, decidability imposes comparable limits on the stable growth of complexity, and that such behavior is necessary for nontrivial evolutionary systems. We show that the undecidability of adapted states imposes novel and unpredictable behavior on the individuals or populations being modeled. Such behavior is irreducible. Finally, we offer an example of a system, first proposed by Chaitin, that exhibits strong OEE.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (1): 10–28.
Published: 01 February 2018
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Artificial life is concerned with understanding the dynamics of human societies. A defining feature of any society is its institutions. However, defining exactly what an institution is has proven difficult, with authors often talking past each other. This article presents a dynamic model of institutions, which views them as political game forms that generate the rules of a group's economic interactions. Unlike most prior work, the framework presented here allows for the construction of explicit models of the evolution of institutional rules. It takes account of the fact that group members are likely to try to create rules that benefit themselves. Following from this, it allows us to determine the conditions under which self-interested individuals will create institutional rules that support cooperation—for example, that prevent a tragedy of the commons. The article finishes with an example of how a model of the evolution of institutional rewards and punishments for promoting cooperation can be created. It is intended that this framework will allow artificial life researchers to examine how human groups can themselves create conditions for cooperation. This will help provide a better understanding of historical human social evolution, and facilitate the resolution of pressing societal social dilemmas.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (1): 29–48.
Published: 01 February 2018
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In both social systems and ecosystems there is a need to resolve potential conflicts between the interests of individuals and the collective interest of the community. The collective interests need to survive the turbulent dynamics of social and ecological interactions. To see how different systems with different sets of interactions have different degrees of robustness, we need to look at their different contingent histories. We analyze abstract artificial life models of such systems, and note that some prominent examples rely on explicitly ahistorical frameworks; we point out where analyses that ignore a contingent historical context can be fatally flawed. The mathematical foundations of Gaia theory are presented in a form whose very basic and general assumptions point to wide applicability across complex dynamical systems. This highlights surprising connections between robustness and accumulated contingent happenstance, regardless of whether Darwinian evolution is or is not implicated. Real-life studies highlight the role of history, and artificial life studies should do likewise.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (1): 71–79.
Published: 01 February 2018
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Protocells are objects that mimic one or several functions of biological cells and may be embodied as solid particles, lipid vesicles, or droplets. Our work is based on using decanol droplets in an aqueous solution of sodium decanoate in the presence of salt. A decanol droplet under such conditions bears many qualitative similarities with living cells, such as the ability to move chemotactically, divide and fuse, or change its shape. This article focuses on the description of a shape-changing process induced by the evaporation of water from the decanoate solution. Under these conditions, the droplets perform complex shape changes, whereby the originally round decanol droplets grow into branching patterns and mimic the growth of appendages in bacteria or axon growth of neuronal cells. We report two outcomes: (i) the morphological changes are reversible, and (ii) multiple protocells avoid contact between each other during the morphological transformation. The importance of these morphological changes in the context of artificial life are discussed.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (4): 518–527.
Published: 01 November 2017
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In Lévy walks (LWs), particles move with a fixed speed along straight line segments and turn in new directions after random time intervals that are distributed according to a power law. Such LWs are thought to be an advantageous foraging and search strategy for organisms. While complex nervous systems are certainly capable of producing such behavior, it is not clear at present how single-cell organisms can generate the long-term correlated control signals required for a LW. Here, we construct a biochemical reaction system that generates long-time correlated concentration fluctuations of a signaling substance, with a tunable fractional exponent of the autocorrelation function. The network is based on well-known modules, and its basic function is highly robust with respect to the parameter settings.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (4): 481–492.
Published: 01 November 2017
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This article suggests that the fundamental haploid-diploid cycle of eukaryotic sex exploits a rudimentary form of the Baldwin effect. With this explanation for the basic cycle, the other associated phenomena can be explained as evolution tuning the amount and frequency of learning experienced by an organism. Using the well-known NK model of fitness landscapes, it is shown that varying landscape ruggedness varies the benefit of the haploid-diploid cycle, whether based upon endomitosis or syngamy. The utility of pre-meiotic doubling and recombination during the cycle are also shown to vary with landscape ruggedness. This view is suggested as underpinning, rather than contradicting, many existing explanations for sex.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (4): 453–480.
Published: 01 November 2017
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One proposed scenario for the emergence of biochemical oscillations is that they may have provided the basic mechanism behind cellular self-replication by growth and division. However, alternative scenarios not requiring any chemical oscillation have also been proposed. Each of the various protocell models proposed to support one or another scenario comes with its own set of specific assumptions, which makes it difficult to ascertain whether chemical oscillations are required or not for cellular self-replication. This article compares these two cases within a single whole-cell model framework. This model relies upon a membrane embedding a chemical reaction network (CRN) synthesizing all the cellular constituents, including the membrane, by feeding from an external nutrient. Assuming the osmolarity is kept constant, the system dynamics are governed by a set of nonlinear differential equations coupling the chemical concentrations and the surface-area-to-volume ratio. The resulting asymptotic trajectories are used to determine the cellular shape by minimizing the membrane bending energy (within an approximate predefined family of shapes). While the stationary case can be handled quite generally, the oscillatory one is investigated using a simple oscillating CRN example, which is used to identify features that are expected to hold for any network. It is found that cellular self-replication can be reached with or without chemical oscillations, and that a requirement common to both stationary and oscillatory cases is that a minimum spontaneous curvature of the membrane is required for the cell to divide once its area and volume are both doubled. The oscillatory case can result in a greater variety of cellular shape trajectories but raises additional constraints for cellular division and self-replication: (i) the ratio of doubling time to oscillation period should be an integer, and (ii) if the oscillation amplitude is sufficiently high, then the spontaneous curvature must be below a maximum value to avoid early division before the end of the cycle. Because of these additional stringent constraints, it is likely that early protocells did not rely upon chemical oscillations. Biochemical oscillations typical of modern evolved cells may have emerged later through evolution for other reasons (e.g., metabolic advantage) and must have required additional feedback mechanisms for such a self-replicating system to be robust against even slight environmental variations (e.g., temperature fluctuations).
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (4): 528–549.
Published: 01 November 2017
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Liquid droplets are very simple objects present in our everyday life. They are extremely important for many natural phenomena as well as for a broad variety of industrial processes. The conventional research areas in which the droplets are studied include physical chemistry, fluid mechanics, chemical engineering, materials science, and micro- and nanotechnology. Typical studies include phenomena such as condensation and droplet formation, evaporation of droplets, or wetting of surfaces. The present article reviews the recent literature that employs droplets as animated soft matter. It is argued that droplets can be considered as liquid robots possessing some characteristics of living systems, and such properties can be applied to unconventional computing through maze solving or operation in logic gates. In particular, the lifelike properties and behavior of liquid robots, namely (i) movement, (ii) self-division, and (iii) group dynamics, will be discussed.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (4): 493–517.
Published: 01 November 2017
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Social learning, defined as the imitation of behaviors performed by others, is recognized as a distinctive characteristic in humans and several other animal species. Previous work has claimed that the evolutionary fixation of social learning requires decision-making cognitive abilities that result in transmission bias (e.g., discriminatory imitation) and/or guided variation (e.g., adaptive modification of behaviors through individual learning). Here, we present and analyze a simple agent-based model that demonstrates that the transition from instinctive actuators (i.e., non-learning agents whose behavior is hardcoded in their genes) to social learners (i.e., agents that imitate behaviors) can occur without invoking such decision-making abilities. The model shows that the social learning of a trait may evolve and fix in a population if there are many possible behavioral variants of the trait, if it is subject to strong selection pressure for survival (as distinct from reproduction), and if imitation errors occur at a higher rate than genetic mutation. These results demonstrate that the (sometimes implicit) assumption in prior work that decision-making abilities are required is incorrect, thus allowing a more parsimonious explanation for the evolution of social learning that applies to a wider range of organisms. Furthermore, we identify genotype-phenotype disengagement as a signal for the imminent fixation of social learners, and explain the way in which this disengagement leads to the emergence of a basic form of cultural evolution (i.e., a non-genetic evolutionary system).
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (3): 424–448.
Published: 01 August 2017
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The importance of individual cells in a developing multicellular organism is well known, but precisely how the individual cellular characteristics of those cells collectively drive the emergence of robust, homeostatic structures is less well understood. For example, cell communication via a diffusible factor allows for information to travel across large distances within the population, and cell polarization makes it possible to form structures with a particular orientation, but how do these processes interact to produce a more robust and regulated structure? In this study we investigate the ability of cells with different cellular characteristics to grow and maintain homeostatic structures. We do this in the context of an individual-based model where cell behavior is driven by an intracellular network that determines the cell phenotype. More precisely, we investigated evolution with 96 different permutations of our model, where cell motility, cell death, long-range growth factor (LGF), short-range growth factor (SGF), and cell polarization were either present or absent. The results show that LGF has the largest positive influence on the fitness of the evolved solutions. SGF and polarization also contribute, but all other capabilities essentially increase the search space, effectively making it more difficult to achieve a solution. By perturbing the evolved solutions, we found that they are highly robust to both mutations and wounding. In addition, we observed that by evolving solutions in more unstable environments they produce structures that were more robust and adaptive. In conclusion, our results suggest that robust collective behavior is most likely to evolve when cells are endowed with long-range communication, cell polarisation, and selection pressure from an unstable environment.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (3): 343–350.
Published: 01 August 2017
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Asimov's three laws of robotics, which were shaped in the literary work of Isaac Asimov (1920–1992) and others, define a crucial code of behavior that fictional autonomous robots must obey as a condition for their integration into human society. While, general implementation of these laws in robots is widely considered impractical, limited-scope versions have been demonstrated and have proven useful in spurring scientific debate on aspects of safety and autonomy in robots and intelligent systems. In this work, we use Asimov's laws to examine these notions in molecular robots fabricated from DNA origami. We successfully programmed these robots to obey, by means of interactions between individual robots in a large population, an appropriately scoped variant of Asimov's laws, and even emulate the key scenario from Asimov's story “Runaround,” in which a fictional robot gets into trouble despite adhering to the laws. Our findings show that abstract, complex notions can be encoded and implemented at the molecular scale, when we understand robots on this scale on the basis of their interactions.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2017) 23 (3): 295–317.
Published: 01 August 2017
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Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system.
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