Bluenome : a novel developmental model for the evolution of artificial agents

The Bluenome Model of Development is introduced. The Bluenome model is a developmental model of Artificial Morphogenesis, inspired by biological development, instantiating a subset of two-dimensional Cellular Automata. The Bluenome model is cast as a general model, one which generates organizational topologies for finite sets of component types, assuming local interactions between components. Its key feature is that there exists no relation between genotopic complexity and phenotopic complexity, implying its potential application in high-dimensional evolutionary problems. The Bluenome model is first applied to a series of application-neutral experiments, in which it is shown experimentally that it is capable of producing robust agents in a reasonable amount of computation. Next, it is applied to an application involving the design of embedded agents. This second series of experiments contrasts the Bluenome model against a model in which there exists a bijective relation between genotype and phenotype, showing that the Bluenome model is capable of performing as well or better in cases of high phenotopic complexity. Additionally, genomes from the Bluenome Model are shown to be capable of re-development in differing environments, retaining many relevant phenotopic properties

Embryomorphic Engineering: Emergent innovation through evolutionary development

Embryomorphic Engineering, a particular instance of Morpho-genetic Engineering, takes its inspiration directly from biological development to create new hardware, software or network architectures by decentralized self-assembly of elementary agents. At its core, it combines three key principles of multicellular embryogenesis: chemical gradient di usion (providing positional information to the agents), gene regulatory networks (triggering their diferentiation into types, thus patterning), and cell division (creating structural constraints, thus reshaping). This chapter illustrates the potential of Embryomorphic Engineering in di erent spaces: 2D/3D physical swarms, which can nd applications in collective robotics, synthetic biology or nan- otechnology; and nD graph topologies, which can nd applications in dis- tributed software and peer-to-peer techno-social networks. In all cases, the speci c genotype shared by all the agents makes the phenotype's complex architecture and function modular, programmable and reproducible

Gardening Cyber-Physical Systems

cote interne IRCAM: Stepney12aNational audienceToday’s artefacts, from small devices to buildings and cities, are, or are becoming, cyber-physical socio-technical systems, with tightly interwoven material and computational parts. Currently, we have to la- boriously build such systems, component by component, and the results are often difficult to maintain, adapt, and reconfigure. Even “soft”ware is brittle and non-trivial to adapt and change. If we look to nature, how- ever, large complex organisms grow, adapt to their environment, and repair themselves when damaged. In this position paper, we present Gro-CyPhy, an unconventional computational framework for growing cyber-physical systems from com- putational seeds, and gardening the growing systems, in order to adapt them to specific needs. The Gro-CyPhy architecture comprises: a Seed Factory, a process for designing specific computational seeds to meet cyber-physical system requirements; a Growth Engine, providing the computational processes that grow seeds in simulation; and a Computational Garden, where mul- tiple seeds can be planted and grown in concert, and where a high-level gardener can shape them into complex cyber-physical systems. We outline how the Gro-CyPhy architecture might be applied to a significant exemplar application: a (simulated) skyscraper, comprising several mutually interdependent physical and virtual subsystems, such as the shell of exterior and interior walls, electrical power and data net- works, plumbing and rain-water harvesting, heating and air-conditioning systems, and building management control systems

Evolutionary design of soft-bodied animats with decentralized control

We show how a biologically inspired model of multicellular development combined with a simulated evolutionary process can be used to design the morphologies and controllers of soft-bodied virtual animats. An animat’s morphology is the result of a developmental process that starts from a single cell and goes through many cell divisions, during which cells interact via simple physical rules. Every cell contains the same genome, which encodes a gene regulatory network (GRN) controlling its behavior. After the developmental stage, locomotion emerges from the coordinated activity of the GRNs across the virtual robot body. Since cells act autonomously, the behavior of the animat is generated in a truly decentralized fashion. The movement of the animat is produced by the contraction and expansion of parts of the body, caused by the cells, and is simulated using a physics engine. Our system makes possible the evolution and development of animats that can run, swim, and actively navigate toward a target in a virtual environment

A Good Number of Forms Fairly Beautiful: An Exploration of Biologically-Inspired Automated Design

Artificial Embryogeny (AE) can be described as the use of a dynamical system as a mid-step in a design process; Through emulating Biological Embryogenesis, we hope to reach levels of complexity and robustness currently impossible. AE is a new field, and suffers from a lack of standards and meaningful means of evaluation. In this document, we review existing work, discussing motivations and merits of existing approaches. Throughout, we argue that a viewpoint which does not regard environment as a primary source of information risks taking a naive view of evolution. We argue that ``complexity'' is vaguely and inconsistently defined, and propose several novel measures; Perhaps the simplest model of AE, the Terminating Cellular Automaton, is introduced, and used to compute and contrast our measures. Next, the Deva family of AE algorithms is introduced, a modular Cellular Automaton-like group. A domain of application from Civil Engineering is chosen as an interpretation of the grown organisms. It is initially shown that it is possible to use a Deva algorithm to evolve Plane Trusses successfully, this interpretation providing a discipline-independent measure of success. A series of empirical experiments is undertaken, showing the relative efficacy and effects of several model-level strategies in the context of the evolution of structural design. Finally, we explore the role of environment as a constraint on development of structural form. We demonstrate a strong resistance to environmental change by successfully re-growing the organisms in new environments, showing that some Deva organisms are adding information from the environment to their overall morphology; This provides an arti?cial analogue to the re-use of genes which characterizes biological development

Augmenting Artificial Development with Local Fitness

Abstract — In biology, the importance of environmental feedback to the process of embryogenesis is well understood. In this paper we explore the introduction of a local fitness to an artificial developmental system, providing an artificial analogue to the natural phenomenon. First, we define a highly simplified model of vasculogenesis, an environment-based toy problem in which we can evaluate our strategies. Since the use of a global fitness function for local feedback is likely too computationally expensive, we introduce the notion of a neighbourhoodbased “local fitness ” function. This local fitness serves as an environmental-feedback guide for the developmental system. The result is a developmental analogue of guided hill-climbing, one which significantly improves the performance of an artificial embryogeny in the evolution of a simplified vascular system. We further evaluate our model in a collection of randomly generated two-dimensional geometries, and show that inclusion of local fitness helps allay some of the problem difficulty in irregular environments. In the process, we also introduce a novel and systematic means of generating bounded, connected two-dimensional geometries. I

Growing adaptive machines: combining development and learning in artificial neural networks

The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a reference for experts. Several contributions provide perspectives and future hypotheses on recent highly successful trains of research, including deep learning, the HyperNEAT model of developmental neural network design, and a simulation of the visual cortex. Other contributions cover recent advances in the design of bio-inspired artificial neural networks, including the creation of machines for classification, the behavioural control of virtual agents, the design of virtual multi-component robots and morphologies, and the creation of flexible intelligence. Throughout, the contributors share their vast expertise on the means and benefits of creating brain-like machines. This book is appropriate for advanced students and practitioners of artificial intelligence and machine learning