“Human-Like Playtesting With Deep Learning”, 2018-08-14 ():
We present an approach to learn and deploy human-like playtesting in computer games based on deep learning from player data. We are able to learn and predict the most “human” action in a given position through supervised learning on a convolutional neural network. Furthermore, we show how we can use the learned network to predict key metrics of new content—most notably the difficulty of levels.
Our player data and empirical data come from Candy Crush Saga (CCS) and Candy Crush Soda Saga (CCSS). However, the method is general and well suited for many games, in particular where content creation is sequential. CCS and CCSS are non-deterministic match-3 puzzle games with multiple game modes spread over a few thousand levels, providing a diverse testbed for this technique.
Compared to Monte Carlo Tree Search (MCTS) we show that this approach increases correlation with average level difficulty, giving more accurate predictions as well as requiring only a fraction of the computation time.
[Keywords: deep learning, convolutional neural network, agent simulation, playtesting, Monte-Carlo tree search]
…In CCS we can now estimate the difficulty of a new level in less than a minute and can easily scale the solution at a low cost. This compares to the previous 7 days needed with human playtesting on each new episode of 15 levels. This completely changes the level design process where level designers have now more freedom to iterate on the design and focus more on innovation and creativity than before. Internally, we have also tried this approach on a game in development using rather limited playtest data. Nevertheless, we were able to train a decent agent, albeit much noisier than in CCS and CCSS, which has helped a lot with the iterative process of game development. Since we ran the experiments presented in this paper we have used the CNN agent for more than a year, for more than 1,000 new levels in CCS. The prediction accuracy has been stable and when new game features have been presented it has been easy to retrain the agent to learn the new feature and continue predicting the difficulty.