Deep interactive evolution

This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike images, they are normally sampled randomly from the learned distribution, providing limited control over the resulting output. On the other hand, interactive evolution has shown promise in creating various artifacts such as images, music and 3D objects, but traditionally relies on a hand-designed evolvable representation of the target domain. The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes do resemble valid domain artifacts). Once such a GAN is trained, the latent vector given as input to the GAN's generator network can be put under evolutionary control, allowing controllable and high-quality image generation. In this paper, we demonstrate the advantage of this novel approach through a user study in which participants were able to evolve images that strongly resemble specific target images.Comment: 16 pages, 5 figures, Published at EvoMUSART EvoStar 201

Evidence for the Protective and Compensatory Functions of Resilience in Children with Intellectual and Developmental Disabilities

Children with intellectual and developmental disabilities (IDD) are more likely to engage in behavior problems than children without IDD. In the present study, we explored whether adverse life experiences and events were related to child behavioral and emotional problems. We also examined whether child resilience would act as a protective factor in this putative association between adverse experiences and child behavioral and emotional problems. Mothers of 310 children with IDD aged between 4 and 15 years old completed a cross-sectional online survey including measures of exposure to adverse life experiences, child resilience, and behavior and emotional problems. In moderated multiple regression models, we found that exposure to adverse life experiences had a positive association with child behavior problems and peer problems and that these associations were moderated by child resilience. Resilience served a protective function—lowering risk of problems for children exposed to adversity. Child resilience also served a compensatory function, being directly associated with fewer conduct and emotional problems and increased pro-social behavior. Child resilience may be an important factor in understanding the behavior and emotional problems of children with IDD. Further, especially longitudinal, research is needed. Interventions designed to increase children’s resilience may be beneficial for children with IDD

A spatially-structured PCG method for content diversity in a Physics-based simulation game

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Localisation and origin of the bacteriochlorophyll-derived photosensitizer in the retina of the deep-sea dragon fish Malacosteus niger

Most deep-sea fish have a single visual pigment maximally sensitive at short wavelengths, approximately matching the spectrum of both downwelling sunlight and bioluminescence. However, Malcosteus niger produces far-red bioluminescence and its longwave retinal sensitivity is enhanced by red-shifted visual pigments, a longwave reflecting tapetum and, uniquely, a bacteriochlorophyllderived photosensitizer. The origin of the photosensitizer, however, remains unclear. We investigated whether the bacteriochlorophyll was produced by endosymbiotic bacteria within unusual structures adjacent to the photoreceptors that had previously been described in this species. However, microscopy, elemental analysis and SYTOX green staining provided no evidence for such localised retinal bacteria, instead the photosensitizer was shown to be distributed throughout the retina. Furthermore, comparison of mRNA from the retina of Malacosteus to that of the closely related Pachystomias microdon (which does not contain a bacterichlorophyll-derived photosensitzer) revealed no genes of bacterial origin that were specifically up-regulated in Malacosteus. Instead up-regulated Malacosteus genes were associated with photosensitivity and may relate to its unique visual ecology and the chlorophyll-based visual system. We also suggest that the unusual longwave-reflecting, astaxanthin-based, tapetum of Malacosteus may protect the retina from the potential cytotoxicity of such a system

Unintended consequences of existential quantifications in biomedical ontologies

<p>Abstract</p> <p>Background</p> <p>The Open Biomedical Ontologies (OBO) Foundry is a collection of freely available ontologically structured controlled vocabularies in the biomedical domain. Most of them are disseminated via both the OBO Flatfile Format and the semantic web format Web Ontology Language (OWL), which draws upon formal logic. Based on the interpretations underlying OWL description logics (OWL-DL) semantics, we scrutinize the OWL-DL releases of OBO ontologies to assess whether their logical axioms correspond to the meaning intended by their authors.</p> <p>Results</p> <p>We analyzed ontologies and ontology cross products available via the OBO Foundry site <url>http://www.obofoundry.org</url> for existential restrictions (<it>someValuesFrom</it>), from which we examined a random sample of 2,836 clauses.</p> <p>According to a rating done by four experts, 23% of all existential restrictions in OBO Foundry candidate ontologies are suspicious (Cohens' <it>κ </it>= 0.78). We found a smaller proportion of existential restrictions in OBO Foundry cross products are suspicious, but in this case an accurate quantitative judgment is not possible due to a low inter-rater agreement (<it>κ </it>= 0.07). We identified several typical modeling problems, for which satisfactory ontology design patterns based on OWL-DL were proposed. We further describe several usability issues with OBO ontologies, including the lack of ontological commitment for several common terms, and the proliferation of domain-specific relations.</p> <p>Conclusions</p> <p>The current OWL releases of OBO Foundry (and Foundry candidate) ontologies contain numerous assertions which do not properly describe the underlying biological reality, or are ambiguous and difficult to interpret. The solution is a better anchoring in upper ontologies and a restriction to relatively few, well defined relation types with given domain and range constraints.</p

Increasing generality in machine learning through procedural content generation

Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden, limit storage space requirements, and enable particular aesthetics, a large number of PCG methods have been devised by game developers. Additionally, researchers have explored adapting methods from machine learning, optimization, and constraint solving to PCG problems. Games have been widely used in AI research since the inception of the field, and in recent years have been used to develop and benchmark new machine learning algorithms. Through this practice, it has become more apparent that these algorithms are susceptible to overfitting. Often, an algorithm will not learn a general policy, but instead a policy that will only work for a particular version of a particular task with particular initial parameters. In response, researchers have begun exploring randomization of problem parameters to counteract such overfitting and to allow trained policies to more easily transfer from one environment to another, such as from a simulated robot to a robot in the real world. Here we review the large amount of existing work on PCG, which we believe has an important role to play in increasing the generality of machine learning methods. The main goal here is to present RL/AI with new tools from the PCG toolbox, and its secondary goal is to explain to game developers and researchers a way in which their work is relevant to AI research