Strategy Games: The Components of A Worthy Opponent

Gomme, Daniel; Bartle, Richard

doi:10.1145/3402942.3403018

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…However, with few exceptions, games have only recently started to be used as a serious domain for human-AI interaction research. For instance, Gomme and Bartle [29] used strategy games to study players' expectations for what they consider to be a worthy AIcontrolled opponent. Along those lines, several researchers proposed using games and playful experiences to help designers and users learn AI [25,59,64].…”

Section: Ai-based Game Design and Player Experiencementioning

confidence: 99%

Player-AI Interaction: What Neural Network Games Reveal About AI as Play

Zhu

Villareale

Javvaji

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Section: Ai-based Game Design and Player Experiencementioning

confidence: 99%

Player-AI Interaction: What Neural Network Games Reveal About AI as Play

Zhu

Villareale

Javvaji

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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“…Bagi beberapa pengembang, membuat game dengan AI adaptif terasa rumit. Hal tersebut dikarenakan banyaknya perubahan situasi dan pilihan aksi yang luas, faktor percabangan yang ekstrem, dan pilihan aksi yang mengubah situasi jauh ke masa depan dimana semua faktor tersebut menghambat teknik AI [5]. AI berfungsi berdasarkan algoritma yang dipakai, sehingga tidak akan bertingkah jauh dari algoritma yang dipakai.…”

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Determining Computer Opponent’s Actions in Strategy Game Using K-Nearest Neighbour Algorithm

Freddy

Mulyana²

2022

JuTISI

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Advances in computer technology allow various devices to complete complex computing, especially in the entertainment industry and the biggest example is games. Strategy game is the type of game that most often gets an Artificial Intelligence or AI system implemented to imitate human behaviour when playing games. Many game AI systems are predictable so players get bored quickly, so adaptive and simple AI is needed to make it easier for game developers. K-Nearest Neighbour is a classification algorithm with supervised learning, this algorithm will be used in this study. The research method tests the level of accuracy in determining the class by providing a sample of data which is divided into training data and test data. The measure of the level of accuracy is calculated using the confusion matrix after the test table is obtained. The results of the study concluded that the K-Nearest Neighbour algorithm can determine computer opponents fairly well. More data samples are needed as data training to increase the level of classification accuracy.

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