26,962 research outputs found

    A Survey of Monte Carlo Tree Search Methods

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    Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

    Developing an Ecologically Valid Measure of Creativity for Children with Autism Spectrum Disorders

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    Overview: The subject of this thesis is creativity in Autism Spectrum Disorders (ASD). Part 1 provides a literature review of studies measuring creative thinking and the quality of creative ideas in children and adults with ASD. Both meta-analytic and narrative techniques are used to synthesise a profile of creativity in ASD. Recommendations are made to address the methodological limitations of the studies and more comprehensively and validly study creative performance in individuals with ASD. Part 2 presents an empirical paper describing the development and piloting of a new ecologically valid measure of creativity in children with ASD. Three tasks are investigated in relation to their psychometric properties: interrater and test-retest reliability; criterion and construct validity; and measure acceptability. Preliminary between-group comparisons are made to explore creative performance in children with and without ASD and observe how task conditions moderate these effects. A critical appraisal of the research project is put forward in Part 3. It offers a number of reflections on the process of developing the creativity tasks and scoring criteria as well as expanding upon limitations of the study. Further, it considers broader conceptual themes relating to research in the fields of creativity and ASD and the parallels with engaging in a creative research process. Finally, recommendations for future development of the task battery are made

    The Impact of Digital Tools on Student Writing and How Writing is Taught in Schools

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    In a survey of Advanced Placement and National Writing Project teachers, a majority say digital tools encourage students to be more invested in their writing by encouraging personal expression and providing a wider audience for their work. Most also say digital tools make teaching writing easier, despite an increasingly ambiguous line between formal and informal writing and students' poor understanding of issues such as plagiarism and fair use

    Marketing Communications and Environmental Turbulence: A Complexity Theory View

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    This paper investigates how the choice of different marketing communications activities is influenced by the nature of the companyñ€ℱs external environment, when viewing the environment through a complexity theory lens. A qualitative, case method, using depth interviews, investigated the marketing communications activities in four companies in order to identify the promotional activities adopted in more successful firms in turbulent and stable environments. The results showed that the more successful company, in a turbulent market, subtly uses some destabilizing promotional activities but also makes use of some stabilizing promotional activities. This paper is of benefit by emphasizing a new way to consider promotional activities in companies

    VGM-RNN: Recurrent Neural Networks for Video Game Music Generation

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    The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music
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