2,623 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
A Bot Approach-Based Capacity Testing Automation for Online Video Games
Online games are a type of computer game that can be accessed using the internet network and played with other players to play the same game. With the advance in online game development, game developers are required to develop games that can be played by many players in one game, especially in the capacity of an online game server. Those needs can be achieved by utilizing bots. However, previous works only conducted bot-based testing for testing network capabilities. In this research, those works will be extended further ftotesting the capacity of a game server. The result of this research suggests that bot approach testing can simulate real players adequately. Other than that, the bot approach also can be scalable. However, the result also suggests that the bot approach still has some limitations as bots cannot simulate the dynamics shown by real players. Special attention is also needed towards clients utilized for executing the bots for them to be scalable
Validation of a Taxonomy for Player Actions with Latency and Network Games
This project was designed to study the validity of a taxonomy to classify the impact of player actions with latency. We utilized a commercial game to simulate latency in a first person shooter match where participants competed against a computer controlled opponent. The participants utilized three different weapons: a shotgun, a rocket launcher, and a sniper rifle. Each weapon was designed to embody different characteristics of the taxonomy axes: precision, impact, and deadline. Overall, we partially confirmed the validity of a previous taxonomy. Our findings fit the taxonomy in regards to the impact of damage and the weapon’s shooting speed on a player’s performance but the results were inconclusive on other aspects of player actions
3D Sensing Character Simulation using Game Engine Physics
Creating visual 3D sensing characters that interact with AI peers and the virtual envi-
ronment can be a difficult task for those with less experience in using learning algorithms
or creating visual environments to execute an agent-based simulation.
In this thesis, the use of game engines was studied as a tool to create and execute vi-
sual simulations with 3D sensing characters, and train game ready bots. The idea was to
make use of the game engine’s available tools to create highly visual simulations without
requiring much knowledge in modeling or animation, as well as integrating exterior agent
simulation libraries to create sensing characters without needing expertise in learning
algorithms. These sensing characters, were be 3D humanoid characters that can perform
the basic functions of a game character such as moving, jumping, and interacting, but
also have simulated different senses in them. The senses that these characters can have
include: touch using collision detection, vision using ray casts, directional sound, smell,
and other imaginable senses. These senses are obtained using different game develop-
ment techniques available in the game engine and can be used as input for the learning
algorithm to help the character learn. This allows the simulation of agents using off-the-
shelf algorithms and using the game engine’s motor for the visualizations of these agents.
We explored the use of these tools to create visual bots for games, and teach them how
to play the game until they reach a level where they can serve as adversaries for real-life
players in interactive games.
This solution was tested using both reinforcement learning and imitation learning
algorithms in an attempt to compare how efficient both learning methods can be when
used to teach sensing game bots in different game scenarios. These scenarios varied in
both objective and environment complexity as well as the number of bots to access how
each solution behaves in different scenarios. In this document is presented a related work
on the agent simulation and game engine areas, followed by a more detailed solution and
its implementation ending with practical tests and its results.Criar visualizações de personagens 3D com sentidos que interagem com colegas de
IA e com o ambiente virtual pode ser uma tarefa difÃcil para programadores com menos
experiência no uso de algoritmos de aprendizagem automática ou na criação de ambientes
visuais para executar simulações baseadas em agentes.
Nesta tese foi estudado o uso de motores de jogos como ferramenta para criar e execu-
tar simulações visuais com personagens 3D, e treinar bots para jogos. A ideia foi usar as
ferramentas disponÃveis do motor de jogos para criar simulações visuais sem exigir muito
conhecimento em modelação ou animação, para além de integrar bibliotecas de simulação
de agentes externas para criar personagens com sentidos sem precisar de conhecimentos
em algoritmos de aprendizagem automática. Estas personagens 3D são humanoides que
podem desempenhar as funções básicas de uma personagem de um jogo como mover,
saltar e interagir, mas também terão simulados neles diferentes sentidos. Os sentidos que
estas personagens podem ter inclui: o tato, colisões, visão, som direcional, olfato e outros
sentidos imagináveis. Estes sentidos são obtidos usando diferentes técnicas de desenvol-
vimento de jogos disponÃveis no motor de jogos, e podem ser usados como inputs para os
algoritmos de aprendizagem automática para ajudar as personagens a aprender.
Esta solução foi testada usando algoritmos de Reinforcement Learning e Imitation Le-
arning, com o intuito de comparar a eficiência de ambos os métodos de aprendizagem
quando usados para ensinar bots de jogos em diferentes cenários. Estes cenários variaram
em complexidade de objetivo e ambiente, e também no número de bots para que se possa
visualizar como cada algoritmo se comporta em diferentes cenários. Neste documento
será apresentado um estado da arte nas áreas de simulação de agentes e motores de jogos,
seguido de uma proposta de solução mais detalhada para este problema
Learning Human Behavior From Observation For Gaming Applications
The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value
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