24,232 research outputs found

    Rapid adaptation of video game AI

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    Affective games:a multimodal classification system

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    Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

    A Toolkit for Exploring Affective Interface Adaptation in Videogames

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    From its humble beginnings back in the early 1960’s the videogame has become one of the most successful form of HCI to date. However if we look more closely at the interactions between the game and gamer it becomes evident little has changed since the advent of SpaceWar back in 1961. These interactions are for the most part static and thus predictable, given a particular set of circumstances a game will always react in one particular manner despite anything the player may actually do. Because of this the expected lifespan of a videogame is inherently dependant on the choices the videogame provides; once all possible avenues have been explored the game loses its appeal. In this paper we focus on adapting techniques used in the field of Affective Computing to solve this stagnation in the videogames market. We describe the development of a software development kit (SDK) that allows the interactions between man and machine to become dynamic entities during play by means of monitoring the player’s physiological condition

    Virtual Reality Games for Motor Rehabilitation

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    This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

    Building Machines That Learn and Think Like People

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    Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

    Developing Artificial Intelligence Agents for a Turn-Based Imperfect Information Game

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    Artificial intelligence (AI) is often employed to play games, whether to entertain human opponents, devise and test strategies, or obtain other analytical data. Games with hidden information require specific approaches by the player. As a result, the AI must be equipped with methods of operating without certain important pieces of information while being aware of the resulting potential dangers. The computer game GNaT was designed as a testbed for AI strategies dealing specifically with imperfect information. Its development and functionality are described, and the results of testing several strategies through AI agents are discussed

    CGAMES'2009

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