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Takashi Ikegami
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Journal Articles
Publisher: Journals Gateway
Artificial Life (2020) 26 (1): 130–151.
Published: 01 April 2020
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Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2020) 26 (1): 1–4.
Published: 01 April 2020
Journal Articles
Publisher: Journals Gateway
Artificial Life (2019) 25 (2): 93–103.
Published: 01 May 2019
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Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving . It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2019) 25 (2): 168–177.
Published: 01 May 2019
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Web services are analogous to living ecosystems in nature, in that they form an artificial ecosystem consisting of many tags and their associated media, such as photographs, movies, and web pages created by human users. In biological ecosystems, we view a tag as a species and a human as a hidden environmental resource. Our study examines the evolution of web services, in particular social tagging systems, with respect to the self-organization of new tags. The evolution of new combinations of tags is analyzed as an open-ended evolution (OEE) index. Tag meaning is computed by types of associated tags, including tags that vary their meanings temporally, which, we argue, are examples of OEE.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2019) 25 (2): 178–197.
Published: 01 May 2019
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We propose an approach to open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of time scales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided into three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2019) 25 (1): 1–3.
Published: 01 April 2019
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Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving . It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the first of two special issues on current research on OEE and on the more general concept of open-endedness. Most of the papers presented in these special issues are elaborations of work presented at the Third Workshop on Open-Ended Evolution, held in Tokyo as part of the 2018 Conference on Artificial Life.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2016) 22 (3): 408–423.
Published: 01 August 2016
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We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1 .
Journal Articles
Publisher: Journals Gateway
Artificial Life (2016) 22 (2): 138–152.
Published: 01 May 2016
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Life on Earth must originally have arisen from abiotic chemistry. Since the details of this chemistry are unknown, we wish to understand, in general, which types of chemistry can lead to complex, lifelike behavior. Here we show that even very simple chemistries in the thermodynamically reversible regime can self-organize to form complex autocatalytic cycles, with the catalytic effects emerging from the network structure. We demonstrate this with a very simple but thermodynamically reasonable artificial chemistry model. By suppressing the direct reaction from reactants to products, we obtain the simplest kind of autocatalytic cycle, resulting in exponential growth. When these simple first-order cycles are prevented from forming, the system achieves superexponential growth through more complex, higher-order autocatalytic cycles. This leads to nonlinear phenomena such as oscillations and bistability, the latter of which is of particular interest regarding the origins of life.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2014) 20 (1): 55–76.
Published: 01 January 2014
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Due to recent advances in synthetic biology and artificial life, the origin of life is currently a hot topic of research. We review the literature and argue that the two traditionally competing replicator-first and metabolism-first approaches are merging into one integrated theory of individuation and evolution. We contribute to the maturation of this more inclusive approach by highlighting some problematic assumptions that still lead to an ximpoverished conception of the phenomenon of life. In particular, we argue that the new consensus has so far failed to consider the relevance of intermediate time scales. We propose that an adequate theory of life must account for the fact that all living beings are situated in at least four distinct time scales, which are typically associated with metabolism, motility, development, and evolution. In this view, self-movement, adaptive behavior, and morphological changes could have already been present at the origin of life. In order to illustrate this possibility, we analyze a minimal model of lifelike phenomena, namely, of precarious, individuated, dissipative structures that can be found in simple reaction-diffusion systems. Based on our analysis, we suggest that processes on intermediate time scales could have already been operative in prebiotic systems. They may have facilitated and constrained changes occurring in the faster- and slower-paced time scales of chemical self-individuation and evolution by natural selection, respectively.
Journal Articles
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Artificial Life (2013) 19 (3_4): 387–400.
Published: 01 October 2013
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Living technology aims to help people expand their experiences in everyday life. The environment offers people ways to interact with it, which we call affordances . Living technology is a design for new affordances. When we experience something new, we remember it by the way we perceive and interact with it. Recent studies in neuroscience have led to the idea of a default mode network , which is a baseline activity of a brain system. The autonomy of artificial life must be understood as a sort of default mode that self-organizes its baseline activity, preparing for its external inputs and its interaction with humans. I thus propose a method for creating a suitable default mode as a design principle for living technology. I built a machine called the mind time machine (MTM), which runs continuously for 10 h per day and receives visual data from its environment using 15 video cameras. The MTM receives and edits the video inputs while it self-organizes the momentary now . Its base program is a neural network that includes chaotic dynamics inside the system and a meta-network that consists of video feedback systems. Using this system as the hardware and a default mode network as a conceptual framework, I describe the system's autonomous behavior. Using the MTM as a testing ground, I propose a design principle for living technology.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2010) 16 (3): 233–243.
Published: 01 July 2010
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We have developed a simple chemical system capable of self-movement in order to study the physicochemical origins of movement. We propose how this system may be useful in the study of minimal perception and cognition. The system consists simply of an oil droplet in an aqueous environment. A chemical reaction within the oil droplet induces an instability, the symmetry of the oil droplet breaks, and the droplet begins to move through the aqueous phase. The complement of physical phenomena that is then generated indicates the presence of feedback cycles that, as will be argued, form the basis for self-regulation, homeostasis, and perhaps an extended form of autopoiesis. We discuss the result that simple chemical systems are capable of sensory-motor coupling and possess a homeodynamic state from which cognitive processes may emerge.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2009) 15 (1): 59–70.
Published: 01 January 2009
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The effect of shapes on self-movement has been studied with an extended model of autopoiesis. Autopoiesis is known as a theory of self-boundary maintenance. In this study, not only the autopoietic generation of the self-boundary, but also the emergence of self-motility, has been examined. As a result of computer simulations, it has been found that different membrane shapes cause different types of self-movement. A kind of chemotaxis has been observed for some shapes. The mechanism of chemotaxis is discussed by studying the internal chemical processes within the shape boundaries.
Journal Articles
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Artificial Life (2007) 13 (3): 259–277.
Published: 01 July 2007
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Animals use diverse forms of communication, from sound signals to body postures. Recent ethological studies have reported a unique syntactic communication of a songbird, the Bengalese finch ( Lonchura striata var. domestica ). Male Bengalese finches sing complex courtship songs, which can be reconstructed by finite automata, and female Bengalese finches prefer complex songs, as opposed to monotonous or random ones. These facts suggest that the song syntaxes of male birds may have evolved as a result of sexual selection by female birds. Inspired by this hypothesis, we developed a communication model that is a system coupling different types of automaton, one for song production by males and another for song evaluation by females. We applied this model to study the evolution of syntactic animal communication in terms of the self-organization of coevolving automata. Three types of courting strategies as well as a relationship between the song syntax and female preference emerged. We argue that despite the simple communication involved, the complexity and diversity of song syntaxes can evolve via diverse female preferences.
Journal Articles
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Artificial Life (2006) 12 (4): 461–485.
Published: 01 October 2006
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We study a system of self-replicating loops in which interaction rules between individuals allow competition that leads to the formation of a hypercycle-like network. The main feature of the model is the multiple layers of interaction between loops, which lead to both global spatial patterns and local replication. The network of loops manifests itself as a spiral structure from which new kinds of self-replicating loops emerge at the boundaries between different species. In these regions, larger and more complex self-replicating loops live for longer periods of time, managing to self-replicate in spite of their slower replication. Of particular interest is how micro-scale interactions between replicators lead to macro-scale spatial pattern formation, and how these macro-scale patterns in turn perturb the micro-scale replication dynamics.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2004) 10 (4): 361–378.
Published: 01 October 2004
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Turn-taking behavior is simulated in a coupled-agents system. Each agent is modeled as a mobile robot with two wheels. A recurrent neural network is used to produce the motor outputs and to hold the internal dynamics. Agents are developed to take turns on a two-dimensional arena by causing the network structures to evolve. Turn taking is established using either regular or chaotic behavior of the agents. It is found that chaotic turn takers are more sensitive in response to inputs from the other agent. Conversely, regular turn takers are comparatively robust against noisy inputs, owing to their restricted dynamics. From many observations, including turn taking with virtual agents, we claim that there is a complementary relationship between robustness and adaptability. Furthermore, by investigating the recoupling of agents from different GA generations, we report the emergence of a new turn-taking behavior. Potential for synthesizing a new form of interaction is another characteristic of chaotic turn takers.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2000) 6 (4): 363–376.
Published: 01 October 2000
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This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1997) 3 (4): 243–260.
Published: 01 October 1997
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The emergence of collective strategies in a prey-predator system is studied. We use the term “collective” in the sense of the collective motion of defense or attack often found in behaviors of animal grotips. In our prey-predator system, both prey and predators move around on a two-dimensional plane, interacting by playing a game; predators can score by touching the backside of a prey. Thresholds are assumed for the scores of both prey and predators. The species with the higher scores can reproduce more, and that with the lower scores will be diminished. As a result, strategies as collective motions are observed; these consist of rotating cluster motions, line formations, disordered but one-way marching, and random swarming. In particular, the strategy of random swarming encourages symbiosis in the sense that it is associated with a low extinction probability for the whole system.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1995) 2 (3): 305–318.
Published: 01 April 1995
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Self-reproduction via description is discussed in a network model of machines and description tapes. Tapes consist of bit strings, which encode the machines' function. A tape is replicated when it is read by adequate machines. Generally, a machine rewrites a tape without doing correct replication. The variation in a reproduced tape is taken as mutation. Because this mutation is caused by a machine's program, we call it active mutation . Which machine is translated from a given tape is dependent on what kind of a machine reads the tape. External noise is introduced in a machine's reading process to make errors. A new reaction pathway is induced by external noise via a machine's error action. We find that the induced pathways will be mimicked deterministically in an emerging core structure. This core structure will remain stable after turning off external noise. Low external noise develops a core structure of a minimal self-replicative loop. When external noise is elevated, a more complex network evolves. Machines containing a complex core network, which has been bred in high external noise, will actively rewrite tapes rather than just replicate them. Self-replication not as an individual but as a network now becomes important.