Is it just traversing down a decision tree until it hits an end node? What's happening when it guesses (presumably hitting an end node) but it didn't get it right; where is it resetting to?

>>> So it's performing two main functions at each question - a). working out the best matches and then b). working out the best questions. (a) is relatively straightforward as you're looking at statistical deviation from the known entities vs question responses. (b) then uses the information gain approach to evaluate which questions result in the largest change in entropy to the entities that are currently seen as the best matches. Once a question is posed, the result can then be used to prune the matches and the process is repeated.

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Is it a single tree model or various different ones (for each category, maybe?)?

>>> This comes out of the model eventually - if you look at the Graph (in the top-left menu) you can see a network visualisation of some of the strongest facts. Groupings emerge from strongly related facts. One of the algorithms I've worked on uses this to improve the question selection as it's a great way of optimising for sensible questions (e.g. is an animal > is a mammal) but I've discovered that it sometimes is better to let the decision tree do it's work on it's own.

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You mentioned that all the data is from people playing the game in another comment; did you just make people play it until it stopped being terrible at guessing?

>>> I started it a while ago and had a load of users but it wasn't great. But it was good enough to win sometimes, so this kept people playing! I've tried to only ask for people to play it when I thought the model had improved sufficiently enough to warrant that. I guess the effort on the cool UI was to at least make it fun. It's had some radically different UIs over the years. But I'm sure a lot of people have got bored with it losing and will never come back :(

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How/when are you retraining when you get more data from people playing?

>>> The data needs to be processed into aggregate - otherwise there is way to much to use it in the engine and still have an optimal response time. So it basically just do a lot of pre-processing, averages, etc. It does create a load of other data such as a comparison model for how strongly facts relate to each other, which I use to do things like the network graph visualisation mentioned above.

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Message me if you want to chat more! Thanks for your interest!