Creating Complex Building Blocks through Generative Representation

One of the main limitations for the functional scalability of computer automated design systems is the representation used for encoding designs. Using computer programs as an analogy, representations can be thought of as having the properties of combination, control-flow and abstraction. We define generative representations as those which have the ability to reuse elements in an encoding through either iteration or abstraction and argue that reuse improves functional scalability by allowing the representation to construct buildingblocks and capture design dependencies. Next we describe GENRE, an evolutionary design system for evolving a variety of different types of designs. Using this system we compare the generative representation against a non-generative representation on evolving tables and robots and show that designs evolved with the generative representation have higher fitness than designs created with the non-generative representation. Further, we show that designs evolved with the generative representation are constructed in a modular way through the reuse of discovered building blocks

Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding

In 1994 Karl Sims showed that computational evolution can produce interesting morphologies that resemble natural organisms. Despite nearly two decades of work since, evolved morphologies are not obviously more complex or natural, and the field seems to have hit a complexity ceiling. One hypothesis for the lack of increased complexity is that most work, including Sims’, evolves morphologies composed of rigid elements, such as solid cubes and cylinders, limiting the design space. A second hypothesis is that the encodings of previous work have been overly regular, not allowing complex regularities with variation. Here we test both hypotheses by evolving soft robots with multiple materials and a powerful generative encoding called a compositional pattern-producing network (CPPN). Robots are selected for locomotion speed. We find that CPPNs evolve faster robots than a direct encoding and that the CPPN morphologies appear more natural. We also find that locomotion performance increases as more materials are added, that diversity of form and behavior can be increased with di↵erent cost functions without stifling performance, and that organisms can be evolved at di↵erent levels of resolution. These findings suggest the ability of generative soft-voxel systems to scale towards evolving a large diversity of complex, natural, multi-material creatures. Our results suggest that future work that combines the evolution of CPPNencoded soft, multi-material robots with modern diversityencouraging techniques could finally enable the creation of creatures far more complex and interesting than those produced by Sims nearly twenty years ago

PVTree

PVTree project software and data files including preprint document before submission

Emerged Coupling of Motor Control and Morphological Development in Evolution of Multi-cellular Animats

A model for co-evolving behavior control and morphological development is presented in this paper. The development of the morphology starts with a single cell that is able to divide or die, which is controlled by a gene regulatory network. The cells are connected by springs and form the morphology of the grown individuals. The movements of animats are resulted from the shrinking and relaxation of the springs connecting the lateral cells on the body morphology. The gene regulatory network, together with the frequency and phase shifts of the spring movements are evolved to maximize the distance that the animats can swim in a given time interval. To facilitate the evolution of swimming animats, a term that awards an elongated morphology is also included in the fitness function. We show that animats with different body-plans emerge in the evolutionary runs and that the evolved movement control strategy is coupled with the body plan

End-user adoption of animated interface agents in everyday work applications

Recognizing the potential contribution that interactive software agents bring to everyday work applications, this paper reports on end-user adoption of animated interface agents in one particular work application environment: Microsoft 1 Office. The paper develops and empirically tests a theoretical model of the factors affecting an end-user’s choice to adopt and utilize such interface agents. From this theoretical model, a survey instrument was adapted and administered to 261 participants, familiar with animated interface agents. Results from a partial least squares (PLS) analysis indicates that a variety of factors are at play, which inhibit or foster a person’s choice to utilize and adopt animated interface agents. Of significance is that: (a) both perceived usefulness and perceived enjoyment are important influencing factors; (b) users with high scores in innovativeness toward information technology are less likely to find animated interface agents enjoyable; (c) individuals with high animation predisposition scores perceive animated interface agents to be more enjoyable; and (d) users who perceive animated interface agents to be more enjoyable also perceive them to be more useful. Such insights can be used to leverage the introduction and rollout of animated interface agents in everyday work applications in ways that promote their avid adoption and use

Efficient automatic design of robots

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show for the first time de-novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks

NeSR - Neuroevolución de Sistemas de Reescritura

Las redes neuronales artificiales evolutivas representan una mejora de las redes neuronales artificiales convencionales donde su capacidad de adaptación se ve favorecida por la aplicación de procesos evolutivos. El presente artículo describe un nuevo método evolutivo denominado NeSR (NeuroEvolución de Sistemas de Reescritura) que permite obtener redes neuronales artificiales representadas a través de codificación indirecta. Para realizar dicha codificación se propone utilizar sistemas de reescritura ya que poseen la capacidad de generar arquitecturas complejas a partir de una representación relativamente pequeña. En base a esta representación, se ha realizado una cuidadosa definición de los operadores de crossover y mutación. El método propuesto ha sido aplicado a dos tipos de problemas diferentes (clasificación y control) como forma de mostrar la capacidad de resolución de la estrategia planteada. Los resultados alcanzados a través de las mediciones realizadas son satisfactorios. Finalmente se presentan las conclusiones y se plantean algunas líneas de trabajo futuras.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

NeSR - Neuroevolución de Sistemas de Reescritura

Las redes neuronales artificiales evolutivas representan una mejora de las redes neuronales artificiales convencionales donde su capacidad de adaptación se ve favorecida por la aplicación de procesos evolutivos. El presente artículo describe un nuevo método evolutivo denominado NeSR (NeuroEvolución de Sistemas de Reescritura) que permite obtener redes neuronales artificiales representadas a través de codificación indirecta. Para realizar dicha codificación se propone utilizar sistemas de reescritura ya que poseen la capacidad de generar arquitecturas complejas a partir de una representación relativamente pequeña. En base a esta representación, se ha realizado una cuidadosa definición de los operadores de crossover y mutación. El método propuesto ha sido aplicado a dos tipos de problemas diferentes (clasificación y control) como forma de mostrar la capacidad de resolución de la estrategia planteada. Los resultados alcanzados a través de las mediciones realizadas son satisfactorios. Finalmente se presentan las conclusiones y se plantean algunas líneas de trabajo futuras.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Obtaining L-systems Rules from Strings

This paper presents a proposal to solve the Inverse Problem of Lindenmayer in the deterministic and free-context L-system grammar class. The proposal of this paper is to show a methodology that can obtain an L-system rule from a string representing the development stage of any object. The strings used in the tests were obtained from known grammars. However, they are dealt with as of having an unknown origin to assure the impartiality of the methodology. The idea presented here consists in the regression of growth of the string analyzed by an algorithm built based on relations of growth obtained from string generated by known deterministic grammars. In the tests carried out, all the strings submitted to the proposed algorithm could be reverted to an L-system rule identical to the original rule used in the synthesis of the string. It is also interesting to observe that the obtaining of these rules occurred practically in real time with tested grammars.Este artigo apresenta uma proposta para solucionar o Problema Inverso de Lindenmayer nas classes de gramáticas L-systems livres de contexto e determinísticas. A abordagem desta proposta pretende mostrar uma metodologia que consegue obter uma regra L-system a partir de uma cadeia de caracteres representante do estágio de desenvolvimento de um objeto qualquer. As cadeias utilizadas nos testes são sintetizadas a partir de gramáticas conhecidas, porém são tratadas como de origem desconhecida para assegurar a imparcialidade da metodologia. A idéia aqui apresentada consiste na regressão do crescimento da cadeia analisada por um algoritmo construído com base nas relações de crescimento obtidas a partir de cadeias geradas por gramáticas determinísticas conhecidas. Nos testes realizados todas as cadeias sub-metidas no algoritmo puderam ser revertidas em uma regra L-system idêntica à regra original utilizada na síntese da cadeia. Também é interessante notar que a obtenção destas regras ocorreu praticamente em tem-po real para gramáticas testadas