844 research outputs found

    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

    Generation and Analysis of Content for Physics-Based Video Games

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    The development of artificial intelligence (AI) techniques that can assist with the creation and analysis of digital content is a broad and challenging task for researchers. This topic has been most prevalent in the field of game AI research, where games are used as a testbed for solving more complex real-world problems. One of the major issues with prior AI-assisted content creation methods for games has been a lack of direct comparability to real-world environments, particularly those with realistic physical properties to consider. Creating content for such environments typically requires physics-based reasoning, which imposes many additional complications and restrictions that must be considered. Addressing and developing methods that can deal with these physical constraints, even if they are only within simulated game environments, is an important and challenging task for AI techniques that intend to be used in real-world situations. The research presented in this thesis describes several approaches to creating and analysing levels for the physics-based puzzle game Angry Birds, which features a realistic 2D environment. This research was multidisciplinary in nature and covers a wide variety of different AI fields, leading to this thesis being presented as a compilation of published work. The central part of this thesis consists of procedurally generating levels for physics-based games similar to those in Angry Birds. This predominantly involves creating and placing stable structures made up of many smaller blocks, as well as other level elements. Multiple approaches are presented, including both fully autonomous and human-AI collaborative methodologies. In addition, several analyses of Angry Birds levels were carried out using current state-of-the-art agents. A hyper-agent was developed that uses machine learning to estimate the performance of each agent in a portfolio for an unknown level, allowing it to select the one most likely to succeed. Agent performance on levels that contain deceptive or creative properties was also investigated, allowing determination of the current strengths and weaknesses of different AI techniques. The observed variability in performance across levels for different AI techniques led to the development of an adaptive level generation system, allowing for the dynamic creation of increasingly challenging levels over time based on agent performance analysis. An additional study also investigated the theoretical complexity of Angry Birds levels from a computational perspective. While this research is predominately applied to video games with physics-based simulated environments, the challenges and problems solved by the proposed methods also have significant real-world potential and applications

    Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

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    Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.Comment: Accepted by SIGGRAPH Asia 2022. Project website: http://assembly.csail.mit.edu

    Designing Tools for the Invisible Art of Game Feel

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    Gaming Business Communities: Developing online learning organisations to foster communities, develop leadership, and grow interpersonal education

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    This paper explores, through observation and testing, what possibilities from gaming can be extended into other realms of human interaction to help bring people together, extend education, and grow business. It uses through action learning within the safety of the virtual world within Massively Multiplayer Online Games. Further, I explore how the world of online gaming provides opportunity to train a wide range of skills through extending Revans’ (1980) learning equation and action inquiry methodology. This equation and methodology are deployed in relation to a gaming community to see if the theories could produce strong relationships within organisations and examine what learning, if any, is achievable. I also investigate the potential for changes in business (e.g., employee and customer relationships) through involvement in the gaming community as a unique place to implement action learning. The thesis also asks the following questions on a range of extended possibilities in the world of online gaming: What if the world opened up to a social environment where people could discuss their successes and failures? What if people could take a real world issue and re‐create it in the safe virtual world to test ways of dealing with it? What education answers can the world of online gaming provide

    Mote: A Musical Adventure Game

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    The Mote development team consisted of five individuals--three programmers and two artists--and intended to make a video game for the iPhone that could be a casual game involving the improvisation of music. The team designed and created two levels of an adventure game called Mote, in which a player explores a dream world as an anthropomorphic whole note named Mote.␣Mote plays music for other characters and influences their emotions to solve in-game problems. Both levels featured full voice acting, music, custom animations, and original scenery, as well as a custom scripting engine, custom physics and graphics engines, a custom sound engine, and a complete level editor. While the achievements were incredible, testing showed that the game-play mechanics could be expanded

    A Serious Games Development Environment

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    Un ambiente per lo sviluppo di Serious Game

    Dagstuhl News January - December 2001

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    "Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

    Non-interactive Interactivity: building a seemingly interactive installation

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    Esta dissertação apresenta uma análise sobre mecanismos de interactividade, particularmente no contexto de instalações interactivas, através de um estudo do estado da arte e de mecanismos de interactividade já compreendidos. Estes informam a construção de uma instalação protótipo tecnicamente não interactiva que causa, a quem a usa, uma sensação de resposta e interactividade, apesar de não haver estes factores no sistema. Espera-se que, com o exemplo deste projecto, depois de uma análise técnica e da experiência dos utilizadores, se chegue a conclusões que demonstrem aplicações práticas de certos mecanismos de interactividade para que, no futuro, mais artistas possam criar instalações e obras de arte interactivas mais interessantes.This dissertation aims to improve the knowledge on the mechanisms of interactivity, particularly in regards to interactive installations, through a study of the state of the art, and of known mechanisms of interactivity. This informs the building of a technically non-interactive prototype installation that causes, to those who use it, a perception of responsiveness and interactivity where there is none. It is hoped that, with the example of this project, after an analysis both technical and through the eyes of the user, conclusions will be reached that will demonstrate practical applications of certain mechanisms of interactivity so that, in the future, more artists can create more interesting installations and interactive pieces of art

    Planning under time pressure

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    Heuristic search is a technique used pervasively in artificial intelligence and automated planning. Often an agent is given a task that it would like to solve as quickly as possible. It must allocate its time between planning the actions to achieve the task and actually executing them. We call this problem planning under time pressure. Most popular heuristic search algorithms are ill-suited for this setting, as they either search a lot to find short plans or search a little and find long plans. The thesis of this dissertation is: when under time pressure, an automated agent should explicitly attempt to minimize the sum of planning and execution times, not just one or just the other. This dissertation makes four contributions. First we present new algorithms that use modern multi-core CPUs to decrease planning time without increasing execution. Second, we introduce a new model for predicting the performance of iterative-deepening search. The model is as accurate as previous offline techniques when using less training data, but can also be used online to reduce the overhead of iterative-deepening search, resulting in faster planning. Third we show offline planning algorithms that directly attempt to minimize the sum of planning and execution times. And, fourth we consider algorithms that plan online in parallel with execution. Both offline and online algorithms account for a user-specified preference between search and execution, and can greatly outperform the standard utility-oblivious techniques. By addressing the problem of planning under time pressure, these contributions demonstrate that heuristic search is no longer restricted to optimizing solution cost, obviating the need to choose between slow search times and expensive solutions
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