153,789 research outputs found
Rule-based Test Generation with Mind Maps
This paper introduces basic concepts of rule based test generation with mind
maps, and reports experiences learned from industrial application of this
technique in the domain of smart card testing by Giesecke & Devrient GmbH over
the last years. It describes the formalization of test selection criteria used
by our test generator, our test generation architecture and test generation
framework.Comment: In Proceedings MBT 2012, arXiv:1202.582
Specialization of the rostral prefrontal cortex for distinct analogy processes
Analogical reasoning is central to learning and abstract thinking. It involves using a more familiar situation (source) to make inferences about a less familiar situation (target). According to the predominant cognitive models, analogical reasoning includes 1) generation of structured mental representations and 2) mapping based on structural similarities between them. This study used functional magnetic resonance imaging to specify the role of rostral prefrontal cortex (PFC) in these distinct processes. An experimental paradigm was designed that enabled differentiation between these processes, by temporal separation of the presentation of the source and the target. Within rostral PFC, a lateral subregion was activated by analogy task both during study of the source (before the source could be compared with a target) and when the target appeared. This may suggest that this subregion supports fundamental analogy processes such as generating structured representations of stimuli but is not specific to one particular processing stage. By contrast, a dorsomedial subregion of rostral PFC showed an interaction between task (analogy vs. control) and period (more activated when the target appeared). We propose that this region is involved in comparison or mapping processes. These results add to the growing evidence for functional differentiation between rostral PFC subregions
A Reference Interpreter for the Graph Programming Language GP 2
GP 2 is an experimental programming language for computing by graph
transformation. An initial interpreter for GP 2, written in the functional
language Haskell, provides a concise and simply structured reference
implementation. Despite its simplicity, the performance of the interpreter is
sufficient for the comparative investigation of a range of test programs. It
also provides a platform for the development of more sophisticated
implementations.Comment: In Proceedings GaM 2015, arXiv:1504.0244
The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases
The present longitudinal study ascertained training-associated transformations in the neural underpinnings of diagnostic reasoning, using a simulation game named “Equine Virtual Farm” (EVF). Twenty participants underwent structural, EVF/task-based and resting-state MRI and diffusion tensor imaging (DTI) before and after completing their training on diagnosing simulated veterinary cases. Comparing playing veterinarian versus seeing a colorful image across training sessions revealed the transition of brain activity from scientific creativity regions pre-training (left middle frontal and temporal gyrus) to insight problem-solving regions post-training (right cerebellum, middle cingulate and medial superior gyrus and left postcentral gyrus). Further, applying linear mixed-effects modelling on graph centrality metrics revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular gyrus and parahippocampus) and reward systems (orbital gyrus, nucleus accumbens and putamen) in driving pre-training diagnostic reasoning; whereas, regions implicated in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the main post-training hubs. Lastly, resting-state and DTI analysis revealed post-training effects within the occipitotemporal semantic processing region. Altogether, these results suggest that simulation-based training transforms diagnostic reasoning in novices from regions implicated in creative semantic processing to regions implicated in improvised rule-based problem-solving
Generative Design in Minecraft (GDMC), Settlement Generation Competition
This paper introduces the settlement generation competition for Minecraft,
the first part of the Generative Design in Minecraft challenge. The settlement
generation competition is about creating Artificial Intelligence (AI) agents
that can produce functional, aesthetically appealing and believable settlements
adapted to a given Minecraft map - ideally at a level that can compete with
human created designs. The aim of the competition is to advance procedural
content generation for games, especially in overcoming the challenges of
adaptive and holistic PCG. The paper introduces the technical details of the
challenge, but mostly focuses on what challenges this competition provides and
why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018
proceedings, as part of the workshop on Procedural Content Generatio
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