Rock-Paper-Scissors WiSARD

Souza, Diego de; Carneiro, Hugo C. C.; França, Felipe M. G.; Lima, Priscila M. V.

doi:10.1109/brics-cci-cbic.2013.38

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Decent disciplines were produced in later generations, and eventually an evolved system created in which, instead of making unbiased decisions, it used the player's behavior in previous games to make concrete decisions in the current game. Another proper way to create an intelligent player of rock-paper-scissors was proposed by de Souza et al (2013). To achieve this goal, they used the WIZARD weightless neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The development of smart players requires that their behavior to be consistent with the behavior of other players. Since creating such a player increases the excitement and difficulty of this game, special attention is paid to this problem in the gaming industry (de Souza et al, 2013). In this paper, a novel method to determine human behavior patterns during rock, paper, and scissors game was presented with high precision.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms

Ghasemi

Roshani

Ali

et al. 2021

ARO

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In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%