Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented.

/pdf/developing-serious-games-for-cultural-heritage-a-state-of-4zaesd1wlb.pdf

Developing serious games for cultural heritage: a state-of-the-art review

Repeated, efficient, and extensive use of prototypes is a vital activity that can make the difference between successful and unsuccessful entry of new products into the competitive world market. In this respect, physical prototyping can prove to be very lengthy and expensive, especially if modifications resulting from design reviews involve tool redesign. The availability and affordability of advanced computer technology has paved the way for increasing utilization of prototypes that are digital and created in computer-based environments, i.e. they are virtual as opposed to being physical. The technology for using virtual prototypes was pioneered and adopted initially by large automotive and aerospace industries. Small-to-medium enterprises (SMEs) in the manufacturing industry also need to take virtual prototyping (VP) technology more seriously in order to exploit the benefits. VP is becoming very advanced and may eventually dominate the product development process. However, physical prototypes will still be required for the near future, albeit less frequently. This paper presents a general survey of the available VP techniques and highlights some of the most important developments and research issues while providing sources for further reference. The purpose of the paper is to provide potential SME users with a broad picture of the field of VP and to identify issues and information relevant to the deployment and implementation of VP technology.

/pdf/a-survey-of-virtual-prototyping-techniques-for-mechanical-310092zeyv.pdf

A survey of virtual prototyping techniques for mechanical product development

A method and system provides transactions and arrangements in virtual world environments. A user can participate in transactions to acquire virtual property and related virtual rights. In some implementations, real-world and virtual parties can be involved in possible transfers and/or transfer revocations involving various types of virtual objects and virtual rights.

Virtual world reversion rights

Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging. Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field.

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.

/pdf/artificial-intelligence-for-precision-medicine-in-4a3ndsosno.pdf

Artificial intelligence for precision medicine in neurodevelopmental disorders.

Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.

Evolving simple programs for playing atari games

Object layout in a large 3D-scene or in a complex virtual environment is a time-consuming and tedious task In order to assist the user in this task, we present a general-purposed constraint-based system The underlying constraint solver is built on a genetic algorithm It is able to process a complex set of constraints, including geometric and pseudo-physics ones Moreover, universal quantifiers and Boolean combination of constraints are allowed to make the layout description easier Finally, to get realistic scenes, objects can take any orientations, not only isothetic ones

Constraint-Based 3D-Object Layout using a Genetic Algorithm

In order to produce diversity in virtual creatures to populate virtual worlds, different techniques exist. Some of these use blocks or sticks. In this morphological approach, blocks and sticks can be considered as organs, which means body parts able to perform different functions. Another approach, artificial embryogenesis, consists in developing organisms from a single cell. In this paper, we propose a bridge between these two approaches : a model that will create creatures with a particular morphology and which is organized in organs. The creature development will start from a single cell. In this paper, we propose a unique model able to produce organisms that perform a specific function and to produce organisms with a user-defined morphology.

From Single Cell to Simple Creature Morphology and Metabolism

In this article, we present our research on the evolution of morphology and behavior of complex creatures in virtual environments. We propose to study the evolution of creatures faced with different situations from crawling and walking to more complex activities like climbing and skating. Our creatures use solid 3D blocks and graphtals like Karl Sims' creatures and a new type of controller based on classifier system. The results constitute a new step toward creatures adapted to more complex environments

/pdf/a-new-step-for-artificial-creatures-hdws53kdyp.pdf

A New Step for Artificial Creatures

This paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artiﬁcial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns.

https://hal.inria.fr/inria-00601778/document

Artificial Gene Regulatory Networks and Spatial Computation: A Case Study

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