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ChicuxBot : genetic algorithm configured behavior network multi-agent for Quake II / ChicuxBot – Sistema Multi Agente de Rede de Comportamento Configurado por Algoritmo Genético para Quake IIAlegretti, Francisco José Prates January 2006 (has links)
Este trabalho descreve a implementação de um sistema multi agente usando Redes de Comportamento configurada por Algoritmos Genéticos. O sistema utiliza o jogo de computador Quake II como o ambiente simulado para os agentes. Redes de Comportamento são utilizadas como o mecanismo de tomada de decisão. Um Algoritmo Genético é utilizado para configurar os parâmetros da Rede de Comportamento. Cada agente é um programa independente que se conecta ao servidor do jogo para realizar tarefas e trocar material genético a fim de evoluir. Os resultados obtidos mostram um ambiente multi agente dinamicamente configurado capaz de evoluir e se adaptar apropriadamente conforme o andamento do jogo. / This work describes the implementation of a multi-agent system using Behavior Networks configured by Genetic Algorithms. The system uses the computer game Quake II as the simulated environment for the agents. Behavior Networks are used as the decision making mechanism. The Genetic Algorithm is used to configure the parameters of the Behavior Network. Each agent of the system is an independent program that connects to the game server to perform tasks and to exchange genetic material in order to evolve. The results obtained indicate a dynamically configured multi-agent system that can evolve and adapt accordingly throughout the course of the game.
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ChicuxBot : genetic algorithm configured behavior network multi-agent for Quake II / ChicuxBot – Sistema Multi Agente de Rede de Comportamento Configurado por Algoritmo Genético para Quake IIAlegretti, Francisco José Prates January 2006 (has links)
Este trabalho descreve a implementação de um sistema multi agente usando Redes de Comportamento configurada por Algoritmos Genéticos. O sistema utiliza o jogo de computador Quake II como o ambiente simulado para os agentes. Redes de Comportamento são utilizadas como o mecanismo de tomada de decisão. Um Algoritmo Genético é utilizado para configurar os parâmetros da Rede de Comportamento. Cada agente é um programa independente que se conecta ao servidor do jogo para realizar tarefas e trocar material genético a fim de evoluir. Os resultados obtidos mostram um ambiente multi agente dinamicamente configurado capaz de evoluir e se adaptar apropriadamente conforme o andamento do jogo. / This work describes the implementation of a multi-agent system using Behavior Networks configured by Genetic Algorithms. The system uses the computer game Quake II as the simulated environment for the agents. Behavior Networks are used as the decision making mechanism. The Genetic Algorithm is used to configure the parameters of the Behavior Network. Each agent of the system is an independent program that connects to the game server to perform tasks and to exchange genetic material in order to evolve. The results obtained indicate a dynamically configured multi-agent system that can evolve and adapt accordingly throughout the course of the game.
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Quake II meets JavaWeiße, Carsten, Stöckel, Rene 25 July 2005 (has links) (PDF)
Vortrag zum 135. Unix-Stammtisch
* Das Sourceforge-Projekt Jake2
* Wie langsam ist Java wirklich?
* 3D-Game-Engine Interna
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Massively Parallel Reinforcement Learning With an Application to Video GamesGoeringer, Tyler 23 August 2013 (has links)
No description available.
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Effects Of Frame Aspect Ratio On The Seismic Performance Improvement Of Panel Strengthening TechniqueOkuyucu, Dilek 01 August 2011 (has links) (PDF)
PC panel strengthening technique was developed in M.E.T.U. Structural Mechanics
Laboratory in order to respond the need of practical and efficient pre-quake seismic
strengthening procedures applicable to RC framed structures. The idea behind the
method is simply to convert the non-structural infills into load bearing structural
elements by gluing PC panels over the existing infill wall surface. The remarkable
advantages of the procedure is not only the considerable amount of seismic
performance improvement but also the simplicity of application, very low levels of
disturbance to the occupants and most importantly, the applicability during service.
A number of PC panel application parameters were experimentally investigated by
previous researchers. The success of PC panel method on seismic performance
improvement of RC frames with different aspect ratios was experimentally investigated
in the present study. Total of fifteen, 1:3 scaled, one-bay, two-storey RC frames were
tested in three various aspect ratio series. Constant axial load was applied to the
columns and reversed cyclic load was applied in the lateral direction. Hollow brick
v
infilled frame and cast-in-place RC infilled frame were the lower and upper bound
reference specimens, respectively.
Seismic performance indicators such as response envelope curves, lateral load carrying
capacities, cumulative energy dissipations, initial stiffness indicators and ductility
values clearly showed the effectiveness of PC panel application over different geometry
of RC frames of concern. Moreover, PC panel application either with rectangular or
with strip shaped PC panels provided seismic performance improvement to be almost
equal to that of cast-in-place RC infill application for all series.
Equivalent diagonal strut concept was followed in analytical studies to simulate the
infills of RC frame openings. The required strut material properties were estimated
from total of eighteen individual wall panel tests. The bond-slip effect, due to
utilization low strength of concrete and plain rebars, was also investigated and
introduced to the analytical frame models. Non-linear push over analysis was
performed for all specimens in OpenSees computer software. The analytical results
were compared with that of experimental response envelopes.
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Quake II meets JavaWeiße, Carsten, Stöckel, Rene 25 July 2005 (has links)
Vortrag zum 135. Unix-Stammtisch
* Das Sourceforge-Projekt Jake2
* Wie langsam ist Java wirklich?
* 3D-Game-Engine Interna
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