Neural network bet selectio on even chances.

[mr.ore]

20k spins just to check I haven't accidentally found a holy grail - no, I did not  .

[mr.ore]

Curve fitted 65k spins from Wiesbaden, it is first 65k from that file with 700k spins which lies around somewhere on the forum. Because I'm adapting the same network, I can say that it knows a lot This time only zero-roulette image.

[mr.ore]

Wiesbaden LoL 

[birdhands]

This is an emotional roller coaster!

[mr.ore]

Found a pdfs with interesting information for anyone interested in the topics.

[warrior]

So I finally found time to create quite naive implemntation of counter propagation neural network. It is the one which is more simple to understand and implement than back propagation, but it's nature prefers more generalization, so that severeral slightly different inputs (for example index of tracked events like red=0, black=1 in last x spins like RBRRB = 0 1 0 0 1) are mapped to same output (for example bet on 0 = red). The aim is to reduce variance on even chances. And  here are first results - could it be a path to that mythical "consistent winnig bet"

Dont understand what the bet seletion is on this.

[mr.ore]

There is a black box (neural network = artificial intelligence) which reads past results and it's own previous decisons and on this basis make a decision where to bet next.

example

past spins |  past bets
R B R B B   |  R B R => BLACKBOX => BET B

It can be trained over past spins so that it "knows" where to bet according to patterns. The trick is that I want to enhance it so that it learn to follow trends. I have several trend following methods but they do not adapt as game change. With this it should be possible to control if the blackbox is learning or not, so I could add some counter that would increase if it starts losing so that it make an attempt to find out short term corellations. Basicaly this is a simlified model of player's brain and the aim is to learn it how to read random.

The spins so far are just a check that the AI can reverse engineer bet selection that wins over set of spins.

[superman]

Many months ago I made a bot into AI it works very well but, the main issue I had was the AI can only work with whats already happened, so I added other switches to it but it still can't cope with the flow of random, very interesting though, keep it up mr.ore

[mr.ore]

Could you describe more specific what your AI was and how did you used it? I'm also thinking of learning AI to switch between several selections or learning it to play progressively. Did you dedicated some outputs of the AI as it's imputs in learing? If not then try it, it improves a lot of things.

[superman]

It was very basic, nothing on the scale you are currently looking at, it updated in realtime, FTL, SAL dependent on series it would switch over during the growth of long series and try to catch a declining series length, it worked very well at spotting 3 out of 4, being, chops RBRBRB, series of 2, series of 3 and series >3 it struggled on the series of 2 every time as thats tough to combat because when it worked out what was happening it was too late, depending on the length of the RRBBRRBBRRBB sequence, if it was a short series of RRBBRRBB then it would catch some hits which kept it sort of ok.

I never set it to learn as I didnt really see much point, random isn't supposed to give the same outputs close together, yes it may from day to day but not during one attack, that's what I thought anyway so never bothered. Maybe you are onto something.

The states I looked for where.

Is the series growing in length after each spin?

Then make a decision on the fly as to which way to bet.

[mr.ore]

What kind of AI was it then? You could set it to learn so that had to be a supervised learing. Was it a fuzzy controller, neural network, perceptron or something else like a simple automata? Could you send me a link which describes your algorithm? I would be interested to see and replicate what you did, if it is interesting and worked for you.

[superman]

It wasnt really an alogrithm I was just watching outcomes and trying to react to them the best I could, we know FTL or SAL works for certain series of events but gets caught on flip flops so I just made a few if statements to react to what was happening at that point in time, nothing fancy or clever. Like I said in my last post the only thing that caught it out was series of 2 as by the time you work out its happening it may stop then it takes a few spins to work out whats starting to happen, it worked perfect on flip flops and series longer than 2 but the terrible twos were tough to catch fast enough.

[Bayes]

mr.ore, have you considered playing around with genetic algorithms? They're good for finding optimisations, in this case we want to optimise our winnings. 

Not too difficult to understand either. There's a good beginner's tutorial here.

[Bayes]

Also, if you're looking at statistical techniques, Gizmo used to go on about Cluster Analysis a lot. Might be worth a look...

[mr.ore]

The problem with genetic algorithms is that you need to represent knowledge somehow. Of course you can use genetic algorithm on anything, even learning a neural network, but they are not adaptive, they can't learn as they go. I tried them to optimize some methods, but the results was also just curve fitting. I had a system where you compute a loss you made and you bet 50% on first bet, 25% on the second an so on. Those values seemed best long term, but in a certain set of spins this might not be true, so I tried with genetic algorithm to find the optimal values and then continue playing for a few spins, did not worked much well. My framework is not much good with those algorithms where it has to be runned several times over same set of spins, I usually save a state to file and then run it again from command line, something like "for i in {1..1000}; do ./run; done", then just ./run and see result in gnuplot.