Data-Driven Analytics for Decision Making in Game Sports

Performance analysis and good decision making in sports is important to maximize chances of winning. Over the last years the amount and quality of data which is available for the analysis has increased enormously due to technical developments like, e.g., of sensor technologies or computer vision technology. However, the data-driven analysis of athletes and team performances is very demanding. One reason is the so called semantic gap of sports analytics. This means that the concepts of coaches are seldomly represented in the data for the analysis. Furthermore, sports in general and game sports in particular present a huge challenge due to its dynamic characteristics and the multi-factorial influences on an athlete’s performance like, e.g., the numerous interaction processes during a match. This requires different types of analyses like, e.g., qualitative analyses and thus anecdotal descriptions of performances up to quantitative analyses with which performances can be described through statistics and indicators. Additionally, coaches and analysts have to work under an enormous time pressure and decisions have to be made very quickly. In order to facilitate the demanding task of game sports analysts and coaches we present a generic approach how to conceptualize and design a Data Analytics System (DAS) for an efficient support of the decision making processes in practice. We first introduce a theoretical model and present a way how to bridge the semantic gap of sports analytics. This ensures that DASs will provide relevant information for the decision makers. Moreover, we show that DASs need to combine qualitative and quantitative analyses as well as visualizations. Additionally, we introduce different query types which are required for a holistic retrieval of sports data. We furthermore show a model for the user-centered planning and designing of the User Experience (UX) of a DAS. Having introduced the theoretical basis we present SportSense, a DAS to support decision making in game sports. Its generic architecture allows a fast adaptation to the individual characteristics and requirements of different game sports. SportSense is novel with respect to the fact that it unites raw data, event data, and video data. Furthermore, it supports different query types including an intuitive sketch-based retrieval and seamlessly combines qualitative and quantitative analyses as well as several data visualization options. Moreover, we present the two applications SportSense Football and SportSense Ice Hockey which contain sport-specific concepts and cover (high-level) tactical analyses

Content-based video indexing for sports applications using integrated multi-modal approach

This thesis presents a research work based on an integrated multi-modal approach for sports video indexing and retrieval. By combining specific features extractable from multiple (audio-visual) modalities, generic structure and specific events can be detected and classified. During browsing and retrieval, users will benefit from the integration of high-level semantic and some descriptive mid-level features such as whistle and close-up view of player(s). The main objective is to contribute to the three major components of sports video indexing systems. The first component is a set of powerful techniques to extract audio-visual features and semantic contents automatically. The main purposes are to reduce manual annotations and to summarize the lengthy contents into a compact, meaningful and more enjoyable presentation. The second component is an expressive and flexible indexing technique that supports gradual index construction. Indexing scheme is essential to determine the methods by which users can access a video database. The third and last component is a query language that can generate dynamic video summaries for smart browsing and support user-oriented retrievals

One Step towards Autonomous AI Agent: Reasoning, Alignment and Planning

The recent development of artificial intelligence (AI) has facilitated the prosperity of foundation models, such as large language models (LLMs) and vision models. The foundation models have reshaped the way people interact with tools to improve productivity and creativity by taking over many use cases where people use conventional computer software. Being aware of the promising emerging abilities observed from the foundation models, a more interactive picture has been envisioned where the foundation models drive a group of AI agents that play different roles to fulfill more diverse and complex tasks, further benefiting human society. Like humans, AI agents should be able to reason and plan over complex tasks, and they should also be well aligned with human preferences and values. Advanced foundation models provide a solid foundation for the implementation of AI agents. However, agents based on the current foundation models have intrinsic limitations inherited from existing foundation models. In addition to hallucination, these foundation models can demonstrate biases presented in their training data, resulting in output that can be discriminatory. LLMs can expose sensitive or personal information embedded in their training data, risking user privacy and security. Finally, due to the generative nature of the prevailing foundation models, it is desirable to incorporate planning modules generatively, so the planning process can be seamlessly accomplished during the generation process. In summary, the gaps between the current state-of-the-art and the goals underscore the need for further efforts to improve the reasoning ability, the alignment of human values and the generative planning ability of the foundation models. My ultimate research goal is to build AI agents that are reliable, unbiased, and capable of planning so that they can be safely and effectively applied in various domains. To achieve this goal, I have divided my research into the following subtasks:1. Knowledge-Enhanced Reasoning that aims to improve the factual accuracy and logical coherence of LLM outputs by integrating external knowledge.2. Minimally Supervised Data Generation and Selection that aims to improve the efficiency of fine-tuning or in-context learning by selecting the most informative training data. 3. Automatic Constitution Discovery and Self-alignment that aims to mitigate the risk of generating incorrect, nonsensical, biased or private information. 4. Agents Planning that aims to enable multi-agent strategic learning by incorporating generative goal-guided planning.In this thesis, I will first emphasize the significance of building such reliable, unbiased, capable-of-planning AI agents, and then introduce four lines of my work, and finally the future challenges and opportunities

An approach to computer-based knowledge representation for the business environment using empirical modelling

The motivation for the thesis arises from the difficulties experienced by business people who are non-programmers with the inflexibilities of conventional packages and tools for model-making. After a review of current business software an argument is made for the need for a new computing paradigm that would offer more support for the way that people actually experience their business activities. The Empirical Mod- elling (EM) approach is introduced as a broad theoretical and practical paradigm for computing that can be viewed as a far-reaching generali- sation of the spreadsheet concept. The concepts and principles of EM emphasise the experiential pro- cesses underlying familiar abstractions and by which we come to iden- tify reliable components in everyday life and, in particular, business activities. The emphasis on experience and on interaction leads to the new claim that EM environments offer a framework for combining propositional, experiential and tacit knowledge in a way that is more accessible and supportive of cognitive processes than conventional computer-based modelling. It is proposed that such environments offer an alternative kind of knowledge representation. Turning to the imple- mentation and development of systems, the difficulties inherent in con- ventional methods are discussed and then the practical aspects of EM, and its potential for system building, are outlined. Finally, a more detailed study is made of Decision Support Systems and the ways in which the EM focus on experience, and knowledge through interaction, can contribute to the representation of qualitative aspects of business activities and their use in a more human-centred, but computer-supported, process of decision making. Illustrations of the practical application of EM methods to the requirements of a deci- sion support environment are given by means of extracts from a num- ber of existing EM models

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Western Oregon University 2018-2019 Course Catalog

https://digitalcommons.wou.edu/coursecatalogs/1021/thumbnail.jp

Western Oregon University 2019-2020 Course Catalog

https://digitalcommons.wou.edu/coursecatalogs/1022/thumbnail.jp

Keys to Play: Music as a Ludic Medium from Apollo to Nintendo

How do keyboards make music playable? Drawing on theories of media, systems, and cultural techniques, Keys to Play spans Greek myth and contemporary Japanese digital games to chart a genealogy of musical play and its animation via improvisation, performance, and recreation. As a paradigmatic digital interface, the keyboard forms a field of play on which the book’s diverse objects of inquiry—from clavichords to PCs and eighteenth-century musical dice games to the latest rhythm-action titles—enter into analogical relations. Remapping the keyboard’s topography by way of Mozart and Super Mario, who head an expansive cast of historical and virtual actors, Keys to Play invites readers to unlock ludic dimensions of music that are at once old and new

Longwood University Catalog 2022-2023

This catalog is also searchable and available online though the registrars office at https://catalog.longwood.edu/, this is the archival copy which may lack some of the formatting of the official web version.https://digitalcommons.longwood.edu/catalogs/1121/thumbnail.jp