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My day job is for an online browser based game, one that is small, with a very small staff. In fact, the majority of our staff are volunteers.

I am focused today on one aspect. I want to create an artificial intelligence system that will analyze our users database and report back on accounts that may possibly be run by the same user--which is clearly against our terms and conditions. This "duping" is the major time drain for our staff, and if I can speed it up by giving them a short list of names to check FIRST, I would.

The problem is, I'm not well versed in artificial intelligence. I understand very very basics, but have not successfully implemented a solution currently. I have been reading up on heuristic searches, specifically A* searches, and I "think" it may be appropriate for what I'm looking for, but I can't be sure.

So my question here is: Using an A* search, would it be possible to accurately analyze two user accounts data such as username, password, email, interactions between accounts, interactions between others, login times, activity times, etc. And if not, do you know of a system that would make it possible to analyze this amount of data, and give a "probability" that two accounts may be run by the same person?

1 Answer

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by (33.1k points)
edited by

There is a technique based on graph theory.

For example

Chip-dumping in online poker. Here, an individual opens multiple new accounts on a poker site and then they can claim the advertised bonus for each account (e.g, the deposit of $100 is matched by a $100 bonus). The bonus has highly restrictive "cash-out rules, generally, a threshold number of hands played before the bonus becomes like cash and can be withdrawn from the player's accounts as cash.

Hope this answer helps you!

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