![]() ![]() Next, we’ll need to process the action list, which is a list of all of the different things a player can do in the game for each time slot. ![]() This would give something like the following for number of unique paths:Īveraged 301,150 unique paths in Bonnibel Date 1.csvĪveraged 4,050 unique paths in Bonnibel Date 2.csvĪveraged 12 unique paths in Bonnibel Date 3.csv ![]() This is an exponential function, which means we can vary by a lot with each additional choice made in a date. Note: This isn’t perfect, because this will just give the number of paths of an average play session. That outputs something like this:Īveraged 11.483 choices in Bonnibel Date 1.csvĪveraged 7.561 choices in Bonnibel Date 2.csvĪveraged 2.252 choices in Bonnibel Date 3.csvĮach choice is generally one of 3 options, so that means that we can translate these numbers to unique paths by taking 3 to the power of the average number of choices. To figure this out, I’m going to write a little AI that just randomly plays each Hush Hush asset 1,000 times, and compute an average number of decisions the player has to make. The first thing we need to do is figure out how many choices a user has in an asset. ![]() Some assumptions will have to be made, but the final result should be at least the correct order of magnitude (ish). This will be a pretty quick blog post that takes a look into how many different paths exist in Hush Hush. ![]()
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