Player Preference and Style in a Leading Mobile Card Game

Abstract

Abstract-Tuning game difficulty prior to release requires careful consideration. Players can quickly lose interest in a game if it is too hard or too easy. Assessing how players will cope prior to release is often inaccurate. However, modern games can now collect sufficient data to perform large scale analysis postdeployment and update the product based on these insights.AI Factory Spades is currently the top rated Spades game in the Google Play store. In collaboration with the developers, we have collected gameplay data from 27 592 games and statistics regarding wins/losses for 99 866 games using Google Analytics. Using the data collected, this study analyses the difficulty and behaviour of an Information Set Monte Carlo Tree Search player we developed and deployed in the game previously [1].The methods of data collection and analysis presented in this study are generally applicable. The same workflow could be used to analyse the difficulty and typical player or opponent behaviour in any game. Furthermore, addressing issues of difficulty or non-human-like opponents post-deployment can positively affect player retention.

Emulating Human Play in a Leading Mobile Card Game

“…In previous work, we collaborated with AI Factory to implement ISMCTS-based AI players [9] and then analysed the data collected to gain insights into the playstyles of both the AI and human players [1]. One conclusion was that in certain contexts the AI players were acting significantly different to human players, as evidenced by differences in the distributions of abstract moves chosen given the game state [1].…”

“…In previous work, we collaborated with AI Factory to implement ISMCTS-based AI players [9] and then analysed the data collected to gain insights into the playstyles of both the AI and human players [1]. One conclusion was that in certain contexts the AI players were acting significantly different to human players, as evidenced by differences in the distributions of abstract moves chosen given the game state [1]. Abstract moves are a hand-coded categorization of the specific cards played in a given trick into more abstract types of moves, using extensive game knowledge and depending on the current state of the game.…”

“…To demonstrate the method, we apply it to a digital implementation of the card game Spades by AI Factory Ltd., the leading commercial implementation available for Android devices, with more than 6.5 million downloads. In previous work with the game's developers, we analysed human gameplay data from 27, 592 games and showed significant differences in the playstyles of the AI controlled by their current implementation of MCTS [9], compared to human players [1]. We then demonstrated first steps towards emulating human play whilst still maintaining equivalent playing strength to an unbiased agent by integrating a human gameplay model into MCTS [10].…”

Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

“…They integrated IS-MCTS with knowledge-based methods to create more engaging play. Cowling et al [46] performed a statistical analysis over 27592 played games on a mobile platform to evaluate the player's difficulty for humans. Devlin et al [47] combined insights from game play data with IS-MCTS to emulate human play.…”

The Analysis of Agent’s Knowledge Based on Game Behavior

“…Some researchers have studied the assignment games [1], some like to study games using logic techniques [2], such as the preference in strategy choice [3], the dynamic logic in extended games [4].As further research goes, the boundary between logic and games is getting increasingly blurring.In game behavior, according to the logical points of view, the reasoning involved in games can be divided into deductive reasoning and inductive reasoning.But both deductive reasoning and inductive reasoning will involve agent's cognitive behavior in the process of interaction with each other, i.e., the agent's choicesin strategy game are based on their common knowledge and background knowledge.In particular, common knowledge has a direct impact on the game agent's behavior.More topics involved are the relationships between common knowledge and agent's background knowledge, how they transform from each other, and the properties of knowledge in game behavior, which will be discussed and explored in this paper.…”