4,291 research outputs found
Virtual Reality Games for Motor Rehabilitation
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
Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art
Procedural content generation (PCG) can be applied to a wide variety of tasks
in games, from narratives, levels and sounds, to trees and weapons. A large
amount of game content is comprised of graphical assets, such as clouds,
buildings or vegetation, that do not require gameplay function considerations.
There is also a breadth of literature examining the procedural generation of
such elements for purposes outside of games. The body of research, focused on
specific methods for generating specific assets, provides a narrow view of the
available possibilities. Hence, it is difficult to have a clear picture of all
approaches and possibilities, with no guide for interested parties to discover
possible methods and approaches for their needs, and no facility to guide them
through each technique or approach to map out the process of using them.
Therefore, a systematic literature review has been conducted, yielding 200
accepted papers. This paper explores state-of-the-art approaches to graphical
asset generation, examining research from a wide range of applications, inside
and outside of games. Informed by the literature, a conceptual framework has
been derived to address the aforementioned gaps
Deep learning for procedural content generation
Summarization: Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.Presented on: Neural Computing and Application
Coevolution of Camouflage
Camouflage in nature seems to arise from competition between predator and
prey. To survive, predators must find prey, and prey must avoid being found.
This work simulates an abstract model of that adversarial relationship. It
looks at crypsis through evolving prey camouflage patterns (as color textures)
in competition with evolving predator vision. During their "lifetime" predators
learn to better locate camouflaged prey. The environment for this 2D simulation
is provided by a set of photographs, typically of natural scenes. This model is
based on two evolving populations, one of prey and another of predators. Mutual
conflict between these populations can produce both effective prey camouflage
and predators skilled at "breaking" camouflage. The result is an open source
artificial life model to help study camouflage in nature, and the perceptual
phenomenon of camouflage more generally.Comment: 16 pages, 20 figure
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
A Review of Artificial Intelligence in the Internet of Things
Humankind has the ability of learning new things automatically due to the capacities with which we were born. We simply need to have experiences, read, study… live. For these processes, we are capable of acquiring new abilities or modifying those we already have. Another ability we possess is the faculty of thinking, imagine, create our own ideas, and dream. Nevertheless, what occurs when we extrapolate this to machines? Machines can learn. We can teach them. In the last years, considerable advances have been done and we have seen cars that can recognise pedestrians or other cars, systems that distinguish animals, and even, how some artificial intelligences have been able to dream, paint, and compose music by themselves. Despite this, the doubt is the following: Can machines think? Or, in other words, could a machine which is talking to a person and is situated in another room make them believe they are talking with another human? This is a doubt that has been present since Alan Mathison Turing contemplated it and it has not been resolved yet. In this article, we will show the beginnings of what is known as Artificial Intelligence and some branches of it such as Machine Learning, Computer Vision, Fuzzy Logic, and Natural Language Processing. We will talk about each of them, their concepts, how they work, and the related work on the Internet of Things fields
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