Abstract
We present an exploratory study comparing human player performance against online and offline AI learning techniques—the Naive Bayes Classifier and Genetic Algorithms, respectively—using a simple turn-based game. Human player performance is also assessed according to gender, age, experience playing games, and boredom level during game sessions. Human players and AI techniques are shown to obtain statistically equivalent score distributions. No gender performance differences were found, although performance seems to decrease with age. To a lesser extent, performance appears to improve with self-assessed experience and boredom levels. This study offers a base for more comprehensive experiments, suggesting various directions for future research.
Original language | English |
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Publication status | Published - 2 Jan 2024 |
Event | Springer Nature - Duration: 2 Jan 2024 → … |
Conference
Conference | Springer Nature |
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Period | 2/01/24 → … |
Keywords
- COMPUTER GAMES
- GENETIC ALGORITHMS