993 resultados para applied game


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This paper presents and validates a methodology for integrating reusable software components in diverse game engines. While conforming to the RAGE com-ponent-based architecture described elsewhere, the paper explains how the interac-tions and data exchange processes between a reusable software component and a game engine should be implemented for procuring seamless integration. To this end, a RAGE-compliant C# software component providing a difficulty adaptation routine was integrated with an exemplary strategic tile-based game “TileZero”. Implementa-tions in MonoGame, Unity and Xamarin, respectively, have demonstrated successful portability of the adaptation component. Also, portability across various delivery platforms (Windows desktop, iOS, Android, Windows Phone) was established. Thereby this study has established the validity of the RAGE architecture and its un-derlying interaction processes for the cross-platform and cross-game engine reuse of software components. The RAGE architecture thereby accommodates the large scale development and application of reusable software components for serious gaming.

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Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers’ sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.

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Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.

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The large upfront investments required for game development pose a severe barrier for the wider uptake of serious games in education and training. Also, there is a lack of well-established methods and tools that support game developers at preserving and enhancing the games’ pedagogical effectiveness. The RAGE project, which is a Horizon 2020 funded research project on serious games, addresses these issues by making available reusable software components that aim to support the pedagogical qualities of serious games. In order to easily deploy and integrate these game components in a multitude of game engines, platforms and programming languages, RAGE has developed and validated a hybrid component-based software architecture that preserves component portability and interoperability. While a first set of software components is being developed, this paper presents selected examples to explain the overall system’s concept and its practical benefits. First, the Emotion Detection component uses the learners’ webcams for capturing their emotional states from facial expressions. Second, the Performance Statistics component is an add-on for learning analytics data processing, which allows instructors to track and inspect learners’ progress without bothering about the required statistics computations. Third, a set of language processing components accommodate the analysis of textual inputs of learners, facilitating comprehension assessment and prediction. Fourth, the Shared Data Storage component provides a technical solution for data storage - e.g. for player data or game world data - across multiple software components. The presented components are exemplary for the anticipated RAGE library, which will include up to forty reusable software components for serious gaming, addressing diverse pedagogical dimensions.

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A multi-sectorial regime of protection including international treaties, conservation and security measures, demand reduction campaigns and quasi-military interventions has been established to protect rhinos. Despite these efforts, the poaching of rhinos and trafficking of rhino horn continue unabated. This dissertation asks why the illegal market in rhinoceros horn is so resilient in spite of the myriad measures employed to disrupt it. A theoretical approach grounded in the sociology of markets is applied to explain the structure and functioning of the illegal market. The project follows flows of rhino horn from the source in southern Africa to illegal markets in Southeast Asia. The multi-sited ethnography included participant observations, interviews and focus groups with 416 informants during fourteen months of fieldwork. The sample comprised of, amongst others, convicted and active rhino poachers, smugglers and kingpins, private rhino breeders and hunting outfitters, African and Asian law enforcement officials, as well as affected local communities and Asian consumers. Court files, CITES trade data, archival materials, newspaper reports and social media posts were also analysed to supplement findings and to verify and triangulate data from interviews, focus groups and observations. Central to the analysis is the concept of “contested illegality”, a legitimization mechanism employed by market participants along the different segments of the horn supply chain. These actors' implicit or explicit contestation of the state-sponsored label of illegality serves as a legitimising and enabling mechanism, facilitating participation in gray or illegal markets for rhino horn. The research identified fluid interfaces between legal, illegal and gray markets, with recurring actors who have access to transnational trade structures, and who also possess market and product knowledge, as well as information about the regulatory regime and its loopholes. It is against the background of colonial, apartheid and neoliberal exploitation and marginalization of local communities that a second argument is introduced: the path dependency of conservation paradigms. Underpinning rhino conservation and regulation are archaic and elitist conservation regimes that discount the potential for harmonious relationships between local communities and wildlife. The increasing militarization of anti-poaching measures and green land grabs are exacerbating the rhino problem by alienating communities further from conservation areas and wild animals. The third argument looks at how actors deal with coordination problems in transnational illegal markets. Resolving the coordination problems of cooperation, value and competition are considered essential to the operation of formal markets. It is argued that the problem of security provides an additional and crucial obstacle to actors transacting in markets. The systematic analysis of flows between the researched sites of production, distribution and consumption of rhino horn shows that the social embeddedness of actors facilitates the flourishing of illegal markets in ways that escape an effective enforcement of CITES regulations.

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A dissertation submitted in fulfillment of the requirements to the degree of Master in Computer Science and Computer Engineering

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Part 8: Business Strategies Alignment

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Attention Deficit-Hyperactivity Disorder is a disease that affects 3 to 5 percent of children globally. Many of those live in areas with very few or no medical professionals qualified to help them. To help assuage this problem a system was developed that allows physicians to accompany their patient’s progress and prescribe treatments. These treatments can be drugs or behavioral exercises. The behavioral exercises were designed in the form of games in order to motivate the patients, children, for the treatment. The system allows the patients to play the prescribed games, under the supervision of their tutors. Each game is designed to improve the patient’s handling of their disease through training in a specific mental component. The objective of this approach is to complement the traditional form of treatment by allowing a physician to prescribe therapeutic games and maintaining the patients under supervision between their regular consultations. The main goal of this project is to provide the patients with a better control of their symptoms that with just traditional therapy. Experimental field tests with children and clinical staff, offer promising results. This research is developed in the context of a financed project involving INESC C (Polytechnic Institute of Leiria delegation), the Santo André Hospital of Leiria, and the start-up company PlusrootOne (that owns the project).

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Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

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The aim of this paper is to consider the emergence of nostalgia videogames in the context of playable game criticism. Mirroring the development of the nostalgia film in cinema, an increasing number of developers are creating videogames that are evocative of past gaming forms, designs, and styles. The primary focus of this paper is to explore the extent to which these nostalgia videogames could be considered games-on-games: games that offer a critical view on game design and development, framed by the nostalgia and cultural memory of both gamers and game developers. Theories of pastiche and parody as applied to literature, film, and art are used to form a basis for the examination of recent nostalgia videogames, all of which demonstrate a degree of reflection on the videogame medium.