这个问题是否适合机器学习——brain.js？

Question

The problem I would like to solve is how to choose the best seats on a train based on some ordered user preferences. eg. whether they'd like a seat facing forwards, backwards (or don't care), whether they'd like a seat at a table or not, whether they need to be near a toilet, luggage rack, buffet car, near the door. Window / Aisle seat. Whether they want the aisle to the left or the right (can be very important some someone with a stuff knee!).

Most customers will specify one or two preferences, other may specify more. For some, being near the toilet might be the most important factor, for others having that table to work at might be the most important.

There may be more than one passenger (although they will share preferences). These should be sat as close to each other as possible. 2 passengers would ideally be sat next to each other, or opposite each other at a table seat. A group of 8 passengers might best be split into 2 groups of 4 or 4 groups of 2...

Position is defined by carriage number (seats in the same carriage are better then seats in different carriages) and by x/y coordinate within that carriage - so easy enough to calculate distance between any pair of seats - but a BIG job to calculate distances between EVERY pair of seats...)

Each [available] seat (pre-filtered by ticket class) will have the above attributes either defined or set to NULL (for unknown - seat facing is often unknown).

So for training I can provide a vast array of example trains and customer preferences with the best balance of preferences version position.

For execution I want to provide a run-time specific array of seats with attributes, a set of user preferences and a set if weighting for those preference (eg. passenger 1 thinks being near toilet is most important, passenger 2 think having a table is most important, passenger 3 think being in the quiet carriage is..) and finally the number of passengers.

Output will be an array of seats (one per passenger) that strike the best compromise between matching as many customer preferences as possible (usually not possible to match all preferences) and keeping the seats fairly close to each other.

eg. We might be able to match 2 preferences with seats 2 rows apart, but match 3 preference with seats 10 rows apart...

Obviously distance will need a weighting the same as the individual preference and necessary to choose between those two. I suppose a distance not greater than X becomes just one more customer preference...

I've not done any ML work before, so it's all going to be a learning exercise for me. I wish I had the time to just play and see what comes out, but I don't, Happy to do that, but I need to have a reasonable expectation of a positive result otherwise I'll have to focus on a more traditional approach. Limited time and all that...

So, my questions are:

1. Is this a suitable problem for machine learning?
2. If so, is brain.js a good choice, or is something else more suitable? AWS ML service perhaps?
3. Any advice on how to organise all my data into something suitable for an ML engine to process?

Answer 1

Machine Learning is good at finding hidden patterns in complex data. In your case, you would need a lot of data where user preferences are already matched with optimal seating arrangements.

You could then try to see if the ML model can actually make optimal seating arrangements by itself. It's an interesting problem but it may also lead to unexpected seating :)

If you don't have training data you could collect it live, by registering where people sit down, knowing their preferences.