Playing the Rock-Paper-Scissors game with a genetic algorithm

Ali, Fathelalem F.; Nakao, Zensho; Chen, Yen‐Wei

doi:10.1109/cec.2000.870372

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…66.66% 83.33% With 20 rounds 1 2 3 1 2 3 1 C: Ali et al, 200060% de Souza et al, 201370% Cenggoro et al, 201440% Salvetti et al, 200746% Ghasemi et al, 202059% Ahmadi et al, 201966% Pozzato et al, 2013 36.65% In this study with MLP neural network 60% With RBF neural network 76.66% Markov upgraded with 20 rounds 75%…”

Section: Viconclusionmentioning

confidence: 74%

“…In the 18 th century, a game called Jon_Ken_Pon was invented in Japan in an attempt to solve this problem, which is currently known as Rock-Paper-Scissors (Hasuda et al, 2007). Until recently, many studies have been accomplished in this field, which we will review in the following: Ali et al (2000) proposed a way to create an intelligent player in the game of rock-paper-scissors using genetic algorithm. In this method, a genetic string is encoded to train the intelligent player.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Rock, paper, and scissors are a hand game which is usually played among two players (Ali et al, 2000). This game describes a cycle between three competing species in a network (Hu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Investigations of the behavior of the human players in this game conducted by scientists revealed that most of them have a predictable behavior pattern. Since the adaptability of the rock, paper, and scissors' game leads to a change in the player's strategy over the course of time, this game can be considered a good sample for testing the theory of behavior of human short-term memory (Ali et al, 2000). In game theory, we observe concepts that can model abstract evolutionary systems very well.…”

Section: Introductionmentioning

confidence: 99%

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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: Viconclusionmentioning

confidence: 74%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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 line with these goals, AI price policies must oblige to competition law just as prices that are set by humans. Both European and German competition law distinguish three possible conducts of infringing the cartel prohibition, see Article 101 (1) Treaty on the Functioning of the European Union ("TFEU") 3 : (a) agreements between undertakings, (b) decisions by associations of undertakings, and (c) concerted practices. Independent undertakings shall independently decide over their market behavior and must not coordinate it with their competitors ("requirement of independence").…”

Section: Infringing the Cartel Prohibitionmentioning

confidence: 99%

Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning

Schlechtinger¹,

Kosack²,

Paulheim³

et al. 2021

Preprint

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Pricing decisions are increasingly made by AI. Thanks to their ability to train with live market data while making decisions on the fly, deep reinforcement learning algorithms are especially effective in taking such pricing decisions. In e-commerce scenarios, multiple reinforcement learning agents can set prices based on their competitor's prices. Therefore, research states that agents might end up in a state of collusion in the long run. To further analyze this issue, we build a scenario that is based on a modified version of a prisoner's dilemma where three agents play the game of rock paper scissors. Our results indicate that the action selection can be dissected into specific stages, establishing the possibility to develop collusion prevention systems that are able to recognize situations which might lead to a collusion between competitors. We furthermore provide evidence for a situation where agents are capable of performing a tacit cooperation strategy without being explicitly trained to do so.

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Winning at Any Cost - Infringing the Cartel Prohibition with Reinforcement Learning

Schlechtinger

Kosack

Paulheim

et al. 2021

Lecture Notes in Computer Science

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Playing the Rock-Paper-Scissors game with a genetic algorithm

Cited by 7 publications

References 6 publications

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

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

Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning

Winning at Any Cost - Infringing the Cartel Prohibition with Reinforcement Learning

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