Artificial Life (ARTIF LIFE)

Publications in this journal

• Article: Different Genetic Algorithms and the Evolution of Specialization: A Study with Groups of Simulated Neural Robots.

  Tomassino Ferrauto, Domenico Parisi, Gabriele Di Stefano, Gianluca Baldassarre
  [show abstract] [hide abstract]
  ABSTRACT: Abstract Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviors, often based on role taking and specialization. These behaviors are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms.
  Artificial Life 03/2013;
• Article: Online Learning in a Chemical Perceptron.

  Peter Banda, Christof Teuscher, Matthew R Lakin
  [show abstract] [hide abstract]
  ABSTRACT: Abstract Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and threshold. Both chemical perceptrons can successfully identify all 14 linearly separable two-input logic functions and maintain high robustness against rate-constant perturbations. We suggest that DNA strand displacement could, in principle, provide an implementation substrate for our model, allowing the chemical perceptron to perform reusable, programmable, and adaptable wet biochemical computing.
  Artificial Life 03/2013;
• Article: The Conduciveness of CA-rule Graphs.

  Valmir C Barbosa
  [show abstract] [hide abstract]
  ABSTRACT: Abstract Given two subsets A and B of nodes in a directed graph, the conduciveness of the graph from A to B is the ratio representing how many of the edges outgoing from nodes in A are incoming to nodes in B. When the graph's nodes stand for the possible solutions to certain problems of combinatorial optimization, choosing its edges appropriately has been shown to lead to conduciveness properties that provide useful insight into the performance of algorithms to solve those problems. Here we study the conduciveness of CA-rule graphs, that is, graphs whose node set is the set of all CA rules given a cell's number of possible states and neighborhood size. We consider several different edge sets interconnecting these nodes, both deterministic and random ones, and derive analytical expressions for the resulting graph's conduciveness toward rules having a fixed number of non-quiescent entries. We demonstrate that one of the random edge sets, characterized by allowing nodes to be sparsely interconnected across any Hamming distance between the corresponding rules, has the potential of providing reasonable conduciveness toward the desired rules. We conjecture that this may lie at the bottom of the best strategies known to date for discovering complex rules to solve specific problems, all of an evolutionary nature.
  Artificial Life 03/2013;
• Article: Quantifying the Evolutionary Self-Structuring of Embodied Cognitive Networks.

  Fabio Bonsignorio
  [show abstract] [hide abstract]
  ABSTRACT: Abstract We outline a possible theoretical framework for the quantitative modeling of networked embodied cognitive systems. We notice that: (1) information self-structuring through sensory-motor coordination does not deterministically occur in ℝ(n) vector space, a generic multivariable space, but in SE(3), the group structure of the possible motions of a body in space; (2) it happens in a stochastic open-ended environment. These observations may simplify, at the price of a certain abstraction, the modeling and the design of self-organization processes based on the maximization of some informational measures, such as mutual information. Furthermore, by providing closed form or computationally lighter algorithms, it may significantly reduce the computational burden of their implementation. We propose a modeling framework that aims to give new tools for the design of networks of new artificial self-organizing, embodied, and intelligent agents and for the reverse engineering of natural ones. At this point, it represents largely a theoretical conjecture, and it has still to be experimentally verified whether this model will be useful in practice.
  Artificial Life 03/2013;
• Article: Robustness, Evolvability, and the Logic of Genetic Regulation.

  Joshua L Payne, Jason H Moore, Andreas Wagner
  [show abstract] [hide abstract]
  ABSTRACT: Abstract In gene regulatory circuits, the expression of individual genes is commonly modulated by a set of regulating gene products, which bind to a gene's cis-regulatory region. This region encodes an input-output function, referred to as signal-integration logic, that maps a specific combination of regulatory signals (inputs) to a particular expression state (output) of a gene. The space of all possible signal-integration functions is vast and the mapping from input to output is many-to-one: For the same set of inputs, many functions (genotypes) yield the same expression output (phenotype). Here, we exhaustively enumerate the set of signal-integration functions that yield identical gene expression patterns within a computational model of gene regulatory circuits. Our goal is to characterize the relationship between robustness and evolvability in the signal-integration space of regulatory circuits, and to understand how these properties vary between the genotypic and phenotypic scales. Among other results, we find that the distributions of genotypic robustness are skewed, so that the majority of signal-integration functions are robust to perturbation. We show that the connected set of genotypes that make up a given phenotype are constrained to specific regions of the space of all possible signal-integration functions, but that as the distance between genotypes increases, so does their capacity for unique innovations. In addition, we find that robust phenotypes are (i) evolvable, (ii) easily identified by random mutation, and (iii) mutationally biased toward other robust phenotypes. We explore the implications of these latter observations for mutation-based evolution by conducting random walks between randomly chosen source and target phenotypes. We demonstrate that the time required to identify the target phenotype is independent of the properties of the source phenotype.
  Artificial Life 02/2013;
• Article: Evolutionary Acquisition of a Mortal Genetic Program: The Origin of an Altruistic Gene.

  Tsutomu Oohashi, Tadao Maekawa, Osamu Ueno, Norie Kawai, Emi Nishina, Manabu Honda
  [show abstract] [hide abstract]
  ABSTRACT: Abstract As part of our research on programmed self-decomposition, we formed the hypothesis that originally immortal terrestrial organisms evolve into ones that are programmed for autonomous death. We then conducted evolutionary simulation experiments in which we examined this hypothesis using an artificial ecosystem that we designed to resemble a terrestrial ecosystem endowed with artificial chemistry. Notable results corroborating our hypothesis were obtained, which showed that mortal organisms emerged from indigenous immortal organisms through mutation; such mortal organisms survived and left behind offspring, albeit very rarely, and, having survived, surpassed immortal organisms without exception. In this article, we report the details of the above findings and also discuss a background framework we previously constructed for approaching altruism.
  Artificial Life 02/2013;
• Article: Formalization, Implementation, and Modeling of Institutional Controllers for Distributed Robotic Systems.

  José N Pereira, Porfírio Silva, Pedro U Lima, Alcherio Martinoli
  [show abstract] [hide abstract]
  ABSTRACT: Abstract The work described is part of a long term program of introducing institutional robotics, a novel framework for the coordination of robot teams that stems from institutional economics concepts. Under the framework, institutions are cumulative sets of persistent artificial modifications made to the environment or to the internal mechanisms of a subset of agents, thought to be functional for the collective order. In this article we introduce a formal model of institutional controllers based on Petri nets. We define executable Petri nets-an extension of Petri nets that takes into account robot actions and sensing-to design, program, and execute institutional controllers. We use a generalized stochastic Petri net view of the robot team controlled by the institutional controllers to model and analyze the stochastic performance of the resulting distributed robotic system. The ability of our formalism to replicate results obtained using other approaches is assessed through realistic simulations of up to 40 e-puck robots. In particular, we model a robot swarm and its institutional controller with the goal of maintaining wireless connectivity, and successfully compare our model predictions and simulation results with previously reported results, obtained by using finite state automaton models and controllers.
  Artificial Life 02/2013;
• Article: Growing and Evolving Soft Robots.

  John Rieffel, Davis Knox, Schuyler Smith, Barry Trimmer
  [show abstract] [hide abstract]
  ABSTRACT: Abstract Completely soft and flexible robots offer to revolutionize fields ranging from search and rescue to endoscopic surgery. One of the outstanding challenges in this burgeoning field is the chicken-and-egg problem of body-brain design: Development of locomotion requires the preexistence of a locomotion-capable body, and development of a location-capable body requires the preexistence of a locomotive gait. This problem is compounded by the high degree of coupling between the material properties of a soft body (such as stiffness or damping coefficients) and the effectiveness of a gait. This article synthesizes four years of research into soft robotics, in particular describing three approaches to the co-discovery of soft robot morphology and control. In the first, muscle placement and firing patterns are coevolved for a fixed body shape with fixed material properties. In the second, the material properties of a simulated soft body coevolve alongside locomotive gaits, with body shape and muscle placement fixed. In the third, a developmental encoding is used to scalably grow elaborate soft body shapes from a small seed structure. Considerations of the simulation time and the challenges of physically implementing soft robots in the real world are discussed.
  Artificial Life 02/2013;
• Article: Many Hands Make Light Work: Further Studies in Group Evolution.

  Nicholas Tomko, Inman Harvey, Nathaniel Virgo, Andrew Philippides
  [show abstract] [hide abstract]
  ABSTRACT: Abstract When niching or speciation is required to perform a task that has several different component parts, standard genetic algorithms (GAs) struggle. They tend to evaluate and select all individuals on the same part of the task, which leads to genetic convergence within the population. The goal of evolutionary niching methods is to enforce diversity in the population so that this genetic convergence is avoided. One drawback with some of these niching methods is that they require a priori knowledge or assumptions about the specific fitness landscape in order to work; another is that many such methods are not set up to work on cooperative tasks where fitness is only relevant at the group level. Here we address these problems by presenting the group GA, described earlier by the authors, which is a group-based evolutionary algorithm that can lead to emergent niching. After demonstrating the group GA on an immune system matching task, we extend the previous work and present two modified versions where the number of niches does not need to be specified ahead of time. In the random-group-size GA, the number of niches is varied randomly during evolution, and in the evolved-group-size GA the number of niches is optimized by evolution. This provides a framework in which we can evolve groups of individuals to collectively perform tasks with minimal a priori knowledge of how many subtasks there are or how they should be shared out.
  Artificial Life 02/2013;
• Article: Staging the Self-Assembly Process: Inspiration from Biological Development.

  Navneet Bhalla, Peter J Bentley, Peter D Vize, Christian Jacob
  [show abstract] [hide abstract]
  ABSTRACT: Abstract One of the practical challenges facing the creation of self-assembling systems is being able to exploit a limited set of fixed components and their bonding mechanisms. The method of staging divides the self-assembly process into time intervals, during which components can be added to, or removed from, an environment at each interval. Staging addresses the challenge of using components that lack plasticity by encoding the construction of a target structure in the staging algorithm itself and not exclusively in the design of the components. Previous staging strategies do not consider the interplay between component physical features (morphological information). In this work we use morphological information to stage the self-assembly process, during which components can only be added to their environment at each time interval, to demonstrate our concept. Four experiments are presented, which use heterogeneous, passive, mechanical components that are fabricated using 3D printing. Two orbital shaking environments are used to provide energy to the components and to investigate the role of morphological information with component movement in either two or three spatial dimensions. The benefit of our staging strategy is shown by reducing assembly errors and exploiting bonding mechanisms with rotational properties. As well, a doglike target structure is used to demonstrate in theory how component information used at an earlier time interval can be reused at a later time interval, inspired by the use of a body plan in biological development. We propose that a staged body plan is one method towards scaling self-assembling systems with many interacting components. The experiments and body plan example demonstrate, as proof of concept, that staging enables the self-assembly of more complex morphologies not otherwise possible.
  Artificial Life 02/2013;
• Article: Special Issue for the 20th Anniversary of the European Conference on Artificial Life (ECAL 2011).

  Tom Lenaerts, Mario Giacobini, Hugues Bersini, Paul Bourgine, Marco Dorigo, René Doursat
  Artificial Life 02/2013;
• Article: Motility at the Origin of Life: Its Characterization and a Model.

  Tom Froese, Nathaniel Virgo, Takashi Ikegami
  [show abstract] [hide abstract]
  ABSTRACT: Abstract 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 impoverished 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.
  Artificial Life 02/2013;
• Article: Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation.

  Elmar A Rückert, Gerhard Neumann
  [show abstract] [hide abstract]
  ABSTRACT: Abstract One key idea behind morphological computation is that many difficulties of a control problem can be absorbed by the morphology of a robot. The performance of the controlled system naturally depends on the control architecture and on the morphology of the robot. Because of this strong coupling, most of the impressive applications in morphological computation typically apply minimalistic control architectures. Ideally, adapting the morphology of the plant and optimizing the control law interact so that finally, optimal physical properties of the system and optimal control laws emerge. As a first step towards this vision, we apply optimal control methods for investigating the power of morphological computation. We use a probabilistic optimal control method to acquire control laws, given the current morphology. We show that by changing the morphology of our robot, control problems can be simplified, resulting in optimal controllers with reduced complexity and higher performance. This concept is evaluated on a compliant four-link model of a humanoid robot, which has to keep balance in the presence of external pushes.
  Artificial Life 11/2012;
• Article: A Fluid-Filled Soft Robot That Exhibits Spontaneous Switching among Versatile Spatiotemporal Oscillatory Patterns Inspired by True Slime Mold.

  Takuya Umedachi, Ryo Idei, Kentaro Ito, Akio Ishiguro
  [show abstract] [hide abstract]
  ABSTRACT: Abstract Behavioral diversity is an essential feature of living systems, enabling them to exhibit adaptive behavior in hostile and dynamically changing environments. However, traditional engineering approaches strive to avoid, or suppress, the behavioral diversity in artificial systems to achieve high performance in specific environments for given tasks. The goals of this research include understanding how living systems exhibit behavioral diversity and using these findings to build lifelike robots that exhibit truly adaptive behaviors. To this end, we have focused on one of the most primitive forms of intelligence concerning behavioral diversity, namely, a plasmodium of true slime mold. The plasmodium is a large amoeba-like unicellular organism that does not possess any nervous system or specialized organs. However, it exhibits versatile spatiotemporal oscillatory patterns and switches spontaneously between these. Inspired by the plasmodium, we build a mathematical model that exhibits versatile oscillatory patterns and spontaneously transitions between these patterns. This model demonstrates that, in contrast to coupled nonlinear oscillators with a well-designed complex diffusion network, physically interacting mechanosensory oscillators are capable of generating versatile oscillatory patterns without changing any parameters. Thus, the results are expected to shed new light on the design scheme for lifelike robots that exhibit amazingly versatile and adaptive behaviors.
  Artificial Life 11/2012;
• Article: Morphological Computation of Multi-gaited Robot Locomotion Based on Free Vibration.

  Murat Reis, Xiaoxiang Yu, Nandan Maheshwari, Fumiya Iida
  [show abstract] [hide abstract]
  ABSTRACT: Abstract In recent years, there has been increasing interest in the study of gait patterns in both animals and robots, because it allows us to systematically investigate the underlying mechanisms of energetics, dexterity, and autonomy of adaptive systems. In particular, for morphological computation research, the control of dynamic legged robots and their gait transitions provides additional insights into the guiding principles from a synthetic viewpoint for the emergence of sensible self-organizing behaviors in more-degrees-of-freedom systems. This article presents a novel approach to the study of gait patterns, which makes use of the intrinsic mechanical dynamics of robotic systems. Each of the robots consists of a U-shaped elastic beam and exploits free vibration to generate different locomotion patterns. We developed a simplified physics model of these robots, and through experiments in simulation and real-world robotic platforms, we show three distinctive mechanisms for generating different gait patterns in these robots.
  Artificial Life 11/2012;
• Article: Emotion as Morphofunctionality.

  Carlos Herrera Pérez, Ricardo Sanz
  [show abstract] [hide abstract]
  ABSTRACT: Abstract We argue for a morphofunctional approach to emotion modeling that can also aid the design of adaptive embodied systems. By morphofunctionality we target the online change in both structure and function of a system, and relate it to the notion of physiology and emotion in animals. Besides the biological intuition that emotions serve the function of preparing the body, we investigate the control requirements that any morphofunctional autonomous system must face. We argue that changes in morphology modify the dynamics of the system, thus forming a variable structure system (VSS). We introduce some of the techniques of control theory to deal with VSSs and derive a twofold hypothesis: first, the loose coupling between two control systems, in charge of action and action readiness, respectively; second, the formation of patterned metacontrol. Emotional phenomena can be seen as emergent from this control setup.
  Artificial Life 11/2012;
• Article: Towards Anthropomimetic Robotics: Development, Simulation, and Control of a Musculoskeletal Torso.

  Steffen Wittmeier, Cristiano Alessandro, Nenad Bascarevic, Konstantinos Dalamagkidis, David Devereux, Alan Diamond, Michael Jäntsch, Kosta Jovanovic, Rob Knight, Hugo Gravato Marques, Predrag Milosavljevic, Bhargav Mitra, Bratislav Svetozarevic, Veljko Potkonjak, Rolf Pfeifer, Alois Knoll, Owen Holland
  [show abstract] [hide abstract]
  ABSTRACT: Abstract Anthropomimetic robotics differs from conventional approaches by capitalizing on the replication of the inner structures of the human body, such as muscles, tendons, bones, and joints. Here we present our results of more than three years of research in constructing, simulating, and, most importantly, controlling anthropomimetic robots. We manufactured four physical torsos, each more complex than its predecessor, and developed the tools required to simulate their behavior. Furthermore, six different control approaches, inspired by classical control theory, machine learning, and neuroscience, have been developed and evaluated via these simulations or in small-scale setups. While the obtained results are encouraging, we are aware that we have barely exploited the potential of the anthropomimetic design so far. But, with the tools developed, we are confident that this novel approach will contribute to our understanding of morphological computation and human motor control in the future.
  Artificial Life 11/2012;