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Open Face Chinese Poker [DEMO #12]

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we we we we want to do we want to know who do you want me to do all the things that of so many and
I want since the 1st microphone I wanted to have music player game and I found 1 that I think works fairly well but no if anyone is familiar with open phase Chinese Volker just so I can explain how the world words with that I have to explain the rules and so you have every player has 3 hands the 1st has 3 cards the other 2 have 5 cards and by drawing cards from the back you have to get combinations and then the hands or score so each has underscored that like poker likable grant versus the respective hand of the opponent and your bottom hand has to be strong stronger than your middle had the middle and has to be stronger than the top half of moreover for every kind of combination that you can get you get a certain amount of points and the reason why I thought that this would work well do you think is that the gene is a fairly simple sequence of 5 steps per game and that I thought that this would be highly generalized so it is pretty computation complex I it's not like perfectly sold so I figured I would make an encoder for cards where you encode the the European code encode the type of the Guardian encode the rank and then you have 26 variables for every position on the field where the there's a card where certain characteristics and then ii basically coded the game and granted to randomly assign moves and see what what we could learn and this is what Google shows for spectacular filler obviously didn't learn anything so I started thinking about life but look
at these 2 hands up for that encoder they're completely different no cards are shared however for a player looking at then we have the same exact thing we have a flashed on the bottom and we have appear here and we have a card that would yield a lot of points but paired but that is higher than the pair here so there's a risk with pairing it because you would get full so I figured that i'll try to create an encoder that captures kind of the same way so the encoder of the state
of the the encoder that I came up with doesn't actually convert any cards to as the artist but it converts information about the state of the game so it has current value for each hand number of cards in each hand whether their pairs with the history draws whether they're flash draws how does your hair your hand compared to your opponent of number of that will complete that every type of hand and so on so it's a fairly fairly long encoded and once again you the algorithm to train on it for a while and right so side once again I'll look into what what what what went wrong and I realize that
random selection doesn't really work the 1st hand with which you get points was 25 to 250 hands and while a player would get points every game if they if they play right so I figured out I would try to make the algorithm selects at least plausible models and see if it could learn better then so I coded the selection of possible moves very simple 1 be always put 12 bottom our card makes quite but put in but only put it there and I wrote 600 lines of very very poorly tested code and also the list of the least biased on a code I've ever written if anybody wants to see something disgusting so this is an
image from moderate success but you have all of what happened was that new was actually able to predict different point values for different starting and ending a different point values for selecting so it started to learn something the thing is I haven't had time to write interfaces to tested versus other machine learning techniques for human so quite it works a little bit can but the game of words but that's just the interesting thing that I did have time to test is
I checked whether the hard-coded moves would work with the card encoder re-encodes every single card separately and it did right so that's the interesting part for me you take is that of that if you think about it for those of you went to the musical lecture but this is an argument for including things like dissonance and core diverse encoding every single node separately yeah so you'll want to get some sleep so I'll be able to properly test the hard coded and to make sure that the work let the algorithm learned for while because it's fairly slow it took me maybe an hour to test 20 thousand so that it takes a while and then develop phase and tested against any questions how few so sorry harmless thing for those of you who are you getting an MBA suggest copying this graph theory really early expertise varies like that so they can see someone actually medium well before just giving a random choices of landowners I don't have the data set like that Amedeo insights who will have begin to you but I portion the data that those that will be very helpful to us with which survey of what's the
sequence you feeding it is it is it the card encoding of the cards or is it trying to predict the action it should do so or what is what is it so so get the way it works is our we have different possible moves that are hard-coded then we simulate placing them on the board created as the origin of the state of the board of and kind of create a prediction of the value at the end of the game from the standard state of the board so you can predict the probability of winning given this particular yeah that that also 1 thing is 1 of the reasons why it may be running slowly these I haven't figured out a way to copy new big memory NuPIC models in memory some actually sitting down to the drive which is necessary but still we that so and I the buy this is just the sum of the color of the but the sequences of the number of part of the plan is part of the so yeah so they're 5 in the game with the 1st term you get 5 cards and then every subsequent during you get 3 cards to which you put on the board and I do this until until you get 3 5 and 5 parts the total 5 turns per given let's say 3
moves in a given the last the sequence of the the last 3 moves you're trying to predict what's the probability of winning and then for analyzing and I'm not predicting the probability of winning so that gene is scored based on combinations that you get some trying to predict the score the score of the and the higher the score the death of ancestry moves and given the last the sequence of these 3 moves what's your prediction of the score and what's your prediction of the score is that what it's doing or yet it but that's what it's doing I haven't actually tested the accuracy of the predictions but the point that I got to is basically but that it's able to at least a French predictions for the part of it in a game system you can then simulate different possible next moves and pick the 1 that has the highest quality of cities it's already stimuli so it's it's already simulating and it is able to differentiate values between move so it knows that some words about the thank you have many pieces as you start from the beats you have the after you could bits of old a lot of so I I think the input vector is about which was 600 that's at 600 active that's but it's not just so for yeah that that the thing is that you could make it a lot better because I kind of just for certain boolean values how many bits you have to assign to them that that's something that I have no systematic way of of doing if you think about that T here thanks to a lot of people have been in the mind of in
Spring (hydrology)
Demo (music)
Open set
Point (geometry)
Musical ensemble
Greatest element
Code
State of matter
Memory card
Characteristic polynomial
Combinational logic
Rule of inference
Open set
Field (computer science)
Video game
Code
Moving average
Ranking
Ranking
Position operator
Memory card
Variable (mathematics)
Sequence
Open set
Computational complexity theory
Berechnungskomplexität
Maxima and minima
Word
Scalar field
Smart card
Phase transition
Game theory
Data type
Point (geometry)
Random number
Algorithm
Randomization
NP-hard
Information
State of matter
Code
Line (geometry)
Point (geometry)
Memory card
Flash memory
Electronic mailing list
Code
Line (geometry)
Number
Position operator
Smart card
Smart card
Code
Right angle
Selectivity (electronic)
Game theory
Data type
Point (geometry)
Axiom of choice
Random number
Randomization
Multiplication sign
Virtual machine
Archaeological field survey
Parameter (computer programming)
Mereology
Transcodierung
Medical imaging
Internet forum
Core dump
Code
Software testing
Subtraction
Metropolitan area network
Algorithm
Interface (computing)
Bit
Prediction
Set (mathematics)
Graph theory
Word
Smart card
Phase transition
Vertex (graph theory)
Software testing
Game theory
Point (geometry)
Read-only memory
Group action
Beat (acoustics)
State of matter
Differential (mechanical device)
Scientific modelling
Combinational logic
Mereology
Number
Term (mathematics)
Code
Physical system
Memory card
Planning
Bit
Prediction
Sequence
Summation
Word
Graph coloring
Vector space
Smart card
output
Whiteboard
Game theory

Metadata

Formal Metadata

Title Open Face Chinese Poker [DEMO #12]
Title of Series 2015 Spring NuPIC Hackathon
Number of Parts 19
Author Alexashenko, Sergey
License CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
DOI 10.5446/18058
Publisher Numenta Platform for Intelligent Computing (NuPIC)
Release Date 2015
Language English

Content Metadata

Subject Area Information technology
Abstract Attempts to teach NuPIC to play a card game. Includes some examples of encoding the state of the game.

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