Exploring Quantitative Methods for Evaluating Sports Games

Developing efficient and effective evaluation strategies for measuring video game quality is an important open problem in today’s game industry. Standard methods within the industry rely on surveys and interviews to evaluate engagement of their games and to uncover design flaws. In this proposal, we investigate different types of methods that a game company can […]

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Developing a Quantitative Triangulation Method for Evaluating Sports Games in Production

The game user research field has been increasingly gaining popularity within the video game industry that is interested in applying scientific research to better understand their audiences and optimize the quality of their game experiences. Quantitative approaches are important due to their utility in generating non-biased data concerning emotional responses, engagement, and in-game behaviors. This […]

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Analyzing User Reviews of Mobile Applications using Natural Language Processing

The proposed project is to research and develop a user review analysis system for mobile games using machine learning and natural language processing techniques. Traditionally, user review analyses are done by humans, which is inefficient and relatively slow. The project will involve research in the specific areas of topic modeling, sentiment analysis and opinion spam […]

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Motion fields with deep reinforcement learning for real-time character animation

Character motion in games and animations often have high requirements of realism, aesthetics, and interactivity. For instance, in soccer simulation games, users control the players to move in different directions and perform actions such as passing and shooting. Modern data-driven approaches like motion fields provide convenient ways to synthesizing natural motions from a given database […]

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