14,320 research outputs found

    Problems in modeling the software development process as an adventure game

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    SESAM (Software Engineering Simulation by Animated Models) is a simulator for practicing the role of a software project manager. Its long term goal is to provide a tool for training CS students. As a research project, SESAM calls for an integrated model of the software development process, reflecting and quantifying many phenomena observed in real software projects. We are currently using the second prototype, which can already demonstrate some rational behaviour. More important, however, were our observations in the process of constructing SESAM. They shed light on the current state of software engineering, and on the applicability of metrics. SESAM is being developed in an evolutionary style by the Software Engineering Department (Lehrstuhl) at Stuttgart University; it is implemented in Smalltalk-80 on Unix-Workstations. This paper concentrates on the fundamental questions raised by the work on SESAM. A more complete description of our work has been published before

    Gaming techniques and the product development process : commonalities and cross-applications

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    The use of computer-based tools is now firmly embedded within the product development process, providing a wide range of uses from visualisation to analysis. However, the specialisation required to make effective use of these tools has led to the compartmentalisation of expertise in design teams, resulting in communication problems between individual members. This paper therefore considers how computer gaming techniques and strategies could be used to enhance communication and group design activities throughout the product design process

    Advances in Teaching & Learning Day Abstracts 2004

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    Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2004

    Deep learning for video game playing

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    In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

    Text-based Adventures of the Golovin AI Agent

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    The domain of text-based adventure games has been recently established as a new challenge of creating the agent that is both able to understand natural language, and acts intelligently in text-described environments. In this paper, we present our approach to tackle the problem. Our agent, named Golovin, takes advantage of the limited game domain. We use genre-related corpora (including fantasy books and decompiled games) to create language models suitable to this domain. Moreover, we embed mechanisms that allow us to specify, and separately handle, important tasks as fighting opponents, managing inventory, and navigating on the game map. We validated usefulness of these mechanisms, measuring agent's performance on the set of 50 interactive fiction games. Finally, we show that our agent plays on a level comparable to the winner of the last year Text-Based Adventure AI Competition

    Expressive recommender systems through normalized nonnegative models

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    We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based recommender systems satisfy three criteria that all recommender systems should ideally satisfy: high predictive power, computational tractability, and expressive representations of users and items. Expressive user and item representations are important in practice to succinctly summarize the pool of customers and the pool of items. In NNMs, user representations are expressive because each user's preference can be regarded as normalized mixture of preferences of stereotypical users. The interpretability of item and user representations allow us to arrange properties of items (e.g., genres of movies or topics of documents) or users (e.g., personality traits) hierarchically
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