scholarly journals Modelando um esconde-esconde no Minecraft utilizando Hidden Markov Model

2019 ◽  
Vol 1 (1) ◽  
pp. 48-52
Author(s):  
Ronaldo E Silva Vieira ◽  
Matheus Rodrigues Leal ◽  
Luiz Chaimowicz

A criação de agentes inteligentes é uma área da inteligência artificial que busca construir entidades capazes de desempenhar ações semelhantes aos seres humanos. Dessa forma, é possível modelar cenários e avaliar a tomada de decisão de entidades envolvidas nesses cenários. Este trabalho consiste na construção de agentes inteligentes para participarem da brincadeira de esconde-esconde. Para isso, é desenvolvida uma abordagem probabilística baseada em um Hidden Markov Model, que utiliza o jogo Minecraft como plataforma para sua aplicação. Os experimentos realizados demonstram que a estratégia desenvolvida permite a interação entre os agentes de forma adequada às regras da brincadeira.

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.


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