Sounds intuitive, but there are gaming researches working on that regard. Two related terms (learnt from IEEE Conference of Games) that come to mind:
1. Game refinement theory. The inventors of this theory see games as if they were evolving species, so this is to describe how game became more interesting, more challenging, more "refined". Personally I don't buy that theory because the series of papers had only a limited number of examples and it is questionable how related statistics were generated (especially the repeatedly occured baselines Go and Mahjong), but nonetheless there is theory on that.
2. Deep Player Behaviory Modeling (DPBM): This is the more interesting one. Game developers want their game to be automatically testable, but the agents are often not ready or not true enough. Says AlphaZero for Go or AlphaStar for StarCraft II, they are impressive ones but super-human, so the agnet's behavior give us little insight on how the quality of the game is and how to further improve the game. With DPBM, the signature of real human play can be captured and reproduced by agents, and thus auto-play testing is possible. Balance, fairness, engagement, etc. can then be used as the indirect keys to reassemble "fun."
Sounds intuitive, but there are gaming researches working on that regard. Two related terms (learnt from IEEE Conference of Games) that come to mind:
1. Game refinement theory. The inventors of this theory see games as if they were evolving species, so this is to describe how game became more interesting, more challenging, more "refined". Personally I don't buy that theory because the series of papers had only a limited number of examples and it is questionable how related statistics were generated (especially the repeatedly occured baselines Go and Mahjong), but nonetheless there is theory on that.
2. Deep Player Behaviory Modeling (DPBM): This is the more interesting one. Game developers want their game to be automatically testable, but the agents are often not ready or not true enough. Says AlphaZero for Go or AlphaStar for StarCraft II, they are impressive ones but super-human, so the agnet's behavior give us little insight on how the quality of the game is and how to further improve the game. With DPBM, the signature of real human play can be captured and reproduced by agents, and thus auto-play testing is possible. Balance, fairness, engagement, etc. can then be used as the indirect keys to reassemble "fun."