AI Factory have addressed these complaints with some success by adding hand-designed and hand-tuned heuristic knowledge to the AI. The AI has been very popular but questions have been raised at times regarding occasional non-human- like behaviour. Previously, we deployed an AI in the game AI Factory Spades. Therefore, collecting data from real users of a game and using this to influence the design and behaviour of the AI can increase players’ enjoyment by making AI-controlled opponents and partners more engaging and more human-like.
To the human player, however, this appears irrational as they are not aware they have already won the game. For example, if an AI can assess far earlier than a player that it has lost the game, it may behave randomly as it knows all moves lead to a loss. This occurs often when AI players believe a number of actions have the same value and, therefore, see no reason to perform a specific one despite tendencies that are expected by humans. DOI: Furthermore, if an AI player performs behaviours distinc- tively uncharacteristic of human players, gamers will notice and their enjoyment of the game will be affected. This work is funded by grants EP/H049061/1 and EP/K039857/1 of the UK Engineering and Physical Sciences Research Council (EPSRC). Rollason is with AI Factory Ltd., Pinner, Middlesex, UK. Powley is with Orange Helicopter Games, York, UK. Whitehouse are with the Department of Computer Science and York Centre for Complex Systems Analysis, University of York, UK. Manuscript received Xxxxx XX, 20xx revised Xxxxx XX, 20xx. Subsequently tweaking the settings and parameters of the AI with regard to this analysis can positively impact player retention. Therefore, analysing player data from games after the AI was deployed is essential to understand how the game’s users are coping. Play testing before deployment can reduce the chances of either of the extreme cases occurring, but cannot guarantee they will not occur.
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Player enjoyment is particularly important in mobile games, where player acquisition is driven largely by user-submitted ratings and word-of-mouth: a few negative reviews can significantly damage a mobile game’s download figures.
Somewhere between these two extremes is a compromise that is essential for successful games. If the AI is too difficult players will give up and leave the game, too easy and the players will become bored and quit the game. I NTRODUCTION When deploying an Artificial Intelligence (AI) in a game, it is challenging to balance the difficulty and the fun suitably to maximise players’ enjoyment. Index Terms -Game Analytics, Data Mining, Artificial Intelli- gence, Monte Carlo Tree Search I. Furthermore, addressing issues of difficulty or non-human-like opponents post-deployment can positively affect player retention. The same workflow could be used to analyse the difficulty and typical player or opponent behaviour in any game. The methods of data collection and analysis presented in this study are generally applicable. 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. 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. AI Factory Spades is currently the top rated Spades game in the Google Play store.
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However, modern games can now collect sufficient data to perform large scale analysis post- deployment and update the product based on these insights. Assessing how players will cope prior to release is often inaccurate. Players can quickly lose interest in a game if it is too hard or too easy. Powley, Daniel Whitehouse, Member, IEEE, and Jeff Rollason Abstract -Tuning game difficulty prior to release requires careful consideration. Cowling, Member, IEEE, Sam Devlin, Edward J. X, MONTH 20XX 1 Player Preference and Style in a Leading Mobile Card Game Peter I. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, VOL.
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