Computing and Information Systems - Theses
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ItemWhat you get is what you see: Decomposing Epistemic Planning using Functional STRIPS( 2019)Epistemic planning --- planning with knowledge and belief --- is essential in many multi-agent and human-agent interaction domains. Most state-of-the-art epistemic planners solve this problem by compiling to propositional classical planning, for example, generating all possible knowledge atoms, or compiling epistemic formula to normal forms.It is noted that the compilations are typically exponentially larger than the original problem. However, these methods become computationally infeasible as problems grow. In addition, those methods only works on propositional variables in discrete domains. In this thesis, we decompose epistemic planning by delegating epistemic logic reasoning to an external solver. We do this by modelling the problem using \emph{functional STRIPS}, which is more expressive than standard STRIPS and supports the use of external, black-box functions within action models. Exploiting recent work that demonstrates the relationship between what an agent `sees' and what it knows, we allow modellers to provide new implementations of externals functions. These define what agents see in their environment, allowing new epistemic logics to be defined without changing the planner. As a result, the capability and flexibility of the epistemic model itself are increased, as our model is able to avoid exponential pre-compilation steps and handle logics from continuous domains.We ran evaluations on well-known epistemic planning benchmarks to compare with an existing state-of-the-art planner, and on new scenarios based on different external functions. The results show that our planner scales significantly better than the state-of-the-art planner which we compared against, and can express problems more succinctly.
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ItemTowards improving the network architecture of GANs and their evaluation methods( 2019)Generative Adversarial Networks (GANs) are a powerful class of generative models. GAN models have recently brought significant success in image synthesis tasks. One key issue concerning GANs is the design of a network architecture that results in high training stability and sample quality. GAN models consist of two distinct neural networks known as the generator and discriminator. Conventional practice is to use a deep convolution architecture for both networks that eliminates fully connected layers from the architecture or restricts their uses to only input and output layers. Our investigation reveals that eliminating fully connected layers from the network architecture of GANs is not the best practice, and more effective GAN architecture can be designed by rather exploiting fully connected layers in the conventional convolution architecture. In this respect, we propose an improved network architecture for GANs that employs multiple fully connected layers in both the generator and discriminator networks. Models based on our proposed architecture learn both faster than the conventional architecture and also generate higher quality of samples. In addition, our proposed architecture demonstrates higher training stability than the conventional architecture in several experimental settings. We demonstrate the effectiveness of our architecture in generating high-fidelity images on four benchmark image datasets. Another key challenge when using GANs is how to best measure their ability to generate realistic data. In this regard, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality. In particular, we propose a new evaluation measure, CrossLID, that assesses the local intrinsic dimensionality (LID) of real-world data with respect to neighborhoods found in GAN-generated samples. Intuitively, CrossLID measures the degree to which manifolds of two data distributions coincide with each other. We compare our proposed measure to several state-of-the-art evaluation metrics. Our experiments show that CrossLID is strongly correlated with the progress of GAN training, is sensitive to mode collapse, is robust to small-scale noise and image transformations, and robust to sample size. One key advantage of the proposed CrossLID metric is the ability to assess mode-wise performance of GAN models. The mode-wise evaluation can be used to assess how well a GAN model has learned the different modes present in the target data distribution. We demonstrate how the proposed mode-wise assessment can be utilized during the GAN training process to detect unlearned modes. This leads us to an effective training strategy for GANs that dynamically mitigate unlearned modes by oversampling them during the training. Experiments on benchmark image datasets show that our proposed training approach achieves better performance scores than the conventional GAN training. In addition, our training approach demonstrates higher stability against mode failures of GANs compared to the conventional training.
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ItemPerformances and publics while watching and live-streaming video games on Twitch.tv( 2019)Twitch.tv is a video live-streaming website that launched in 2011 with content centred mostly, but not exclusively, on the playing of video games. Streamers or broadcasters play games in real-time often accompanied by a face camera and audio, while viewers or audiences watch them and interact through a text chat. This study responds to the small, but growing literature surrounding Twitch, and addresses the relative lack of ethnographic research on the topic. Previous research on the platform has focussed thus far on technical aspects of the platform, however user-focused qualitative research on the platform has started to emerge, making this research both timely and relevant. This thesis considers how, and to what extent, the social practices of users contribute to the concepts of ‘networked publics’ and ‘social performance’. It draws on the work of danah boyd and Erving Goffman and considers the usefulness of their theoretical contributions to help contextualise the forms and amendments associated with platforms like Twitch. The analysis emerges from an ethnographic study conducted completely online that features reflexive participant observation, semi-structured, open-ended interviews conducted via email, and in-depth observations of participants’ channels. The thesis is divided into three thematically-organised main data chapters that then feed into a discussion that draws them together to consider a larger conceptual framework. The first such data chapter, ‘Twitch as a Social Media Platform’, argues that the platform demonstrates its role as a social networking site through evidence of matchmaking and mental health. The second main chapter, ‘Twitch as a hobby-profession’, addresses casual and serious leisure and considers the platform in terms of personal investment, branding, and streamer motivation. The third main chapter, ‘Interactions of Streamers and Viewers’, considers the different types of interactions displayed between various users including parasocal relationships and how audiences may hold power on Twitch. Overall, the thesis offers insight into platform use and it characterises Twitch as a user-led participatory space for like-minded individuals who interact in particular ways in a shared community of practice. The interactions exist along a flexible continuum of differing levels of intimacy where users can lurk, actively participate, and network on both personal and professional levels. Audiences are critical for the platform to function, for communities to flourish, and for streamer success. Streamers build rapport and construct ‘authentic’ brands to attract viewers and promote loyalty and sincerity, and users are seen to actively shape and shift extant social structures and practices over time. Ultimately, users find meaning, produce a sense of community belonging, forge social networks, and shape their own identities in relation to others. The thesis concludes that Twitch somewhat paradoxically is both fleeting and robustly sustained by its contemporary community of practice. This community is produced and maintained through interaction and performance that shapes the construction of Twitch’s publics, with Twitch itself acting as a large participatory public as well. Performative sociality and networking are understood as key driving forces for Twitch, offering a rewarding space to make relationships, participate in self-care, share in leisure, and build potential livelihoods, with entertainment becoming a pleasing secondary function.