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 Post subject: Re: Commonsense Go
Post #21 Posted: Thu Aug 18, 2016 6:08 pm 
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Uberdude & Baudis make a simple category error that "Yes, Minister" calls 'Politician's Logic':

Sir Arnold: "Hacker's just grasping at straws. It's the old logical fallacy: All cats have four legs; my dog has four legs .... "
Sir Humphrey: ".... therefore my dog is a cat!".

GnuGo uses pattern matching to identify candidate moves
http://www.gnu.org/software/gnugo/gnugo_4.html#SEC41 [see paragraph "shapes ()" ]

Whereas Commonsense Go uses hierarchical reasoning about abstract concepts to form plans to deduce moves that satisfy goals.

The two methods are explained in my video "Over the Horizon with AHA".

GnuGo is an example of a bottom-up approach to AI, whereas Commonsense Go is a top-down approach:
http://www.webofstories.com/play/marvin.minsky/26

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 Post subject: Re: Commonsense Go
Post #22 Posted: Fri Aug 19, 2016 1:59 am 
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Let's make this simple:

GnuGo uses the concept of groups in its design.

Commonsense Go uses the concept of groups in its design.

AlphaGo does not use the concept of groups in its design.

(and just in case AlphaGo does have groups in there somewhere, although not as core part):
The Go AI at https://chrisc36.github.io/deep-go/ is a pure neural net move selector bot (no MCTS or value network as in AlphaGo) so definitely doesn't have groups in its design.

Thus the bolded claim below is false. If you disagree with this simple logical deduction please explain which part you think is wrong.
djhbrown wrote:
Uberdude wrote:
you'd be making a bot similar to GnuGo

no, you wouldn't. [snip] there is no similarity between the programs whatsoever that is not also shared by Alphago and every other bot ever written to play Go. Not a single jot. If anyone can find one, do tell.


Note that I am not saying your approach is identical to GnuGo, but similar, and more similar than MCTS or Neural Net approaches. That's not to say such a higher-level conceptual approach to Go playing will never be successful at making a pro-level player (and could be more useful as a teaching tool for weak players), maybe it will some years in the future. But an awareness of the shortcomings of similar approaches so far makes it obvious that the claim your rudimentary design just needs to be implemented in software and could then beat AlphaGo is quite frankly ludicrous.
djhbrown wrote:
The program is designed, but it's up to others to turn it into software. Could it beat Alphago across the board, and with much less hardware? I think it could.


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 Post subject: Re: Commonsense Go
Post #23 Posted: Fri Aug 19, 2016 5:08 am 
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Let's go really high level approach here and just boil this down to a winGame() function. Now it's everybody else's fault this hasn't been implemented yet.


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 Post subject: Re: Commonsense Go
Post #24 Posted: Fri Aug 19, 2016 5:13 am 
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Satorian wrote:
Let's go really high level approach here and just boil this down to a winGame() function. Now it's everybody else's fault this hasn't been implemented yet.


I've been trying to write this function for years, but sadly have merely been perfecting blunder().

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 Post subject: Re: Commonsense Go
Post #25 Posted: Fri Aug 19, 2016 7:09 am 
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I work at a software company. My manager sometimes says, "What's not checked in doesn't exist."

It's not to say that research, in itself, is not useful. Indeed, many products we make are inspired by research. But sometimes it's difficult to appreciate the problems in a design if you don't try to implement it.

It's kind of like studying go and coming up with new theories... without ever playing games.

Start winning games, and people will start to consider your ideas more seriously ;-)

Click Here To Show Diagram Code
[go]$$c
$$ ---------------------------------------
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . X . . . . . . . . . . |
$$ | . . X , . . . . . , . . . . . X . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . O . . . . . , . . . . . , . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . X . . . . . . . . . . . . . O . . |
$$ | . . . . . . . . . . . . . . . B . . . |
$$ | . . . O . . . . . , . . X . X , O . . |
$$ | . . . . . O . . . . . . . X O O . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ | . . . . . . . . . . . . . . . . . . . |
$$ ---------------------------------------[/go]

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 Post subject: Re: Commonsense Go
Post #26 Posted: Sat Aug 20, 2016 4:15 pm 
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re: post number 21:
Quote:
please explain which part you think is wrong


simply put, your axiom is a category error and your syllogism is ignoratio elenchi.

you are not the only one; there are others who can't tell the difference between chalk and cheese either. For example, see the comment dialogue here:
https://www.youtube.com/watch?v=UZa2ckl ... nbnttiwg04

History is full of such misunderstandings, and megalomaniacal psychopaths blinded by faith will even kill or excommunicate to sequester territory to protect their hyperactive egos.

You are looking at 4 different things but can only see two kinds of thing: old and new.

Let's call them S, G, M and C.

S has a long and draughty history. G was an imaginative attempt to step out of S's straightjacket, but suffered from myopia, so it never performed better than S.

About 20 years later, M came along, and took Amsterdam by storm, pounding S and G into the dust.

Casual observers began to see the world in two colours: M and not-M.

Then a variant of M - let's call her A - enters the scene in a flurry of publicity and trounces all before her, including poor old L.

Now all you can see is A and not-A.

Then along comes C. C is clearly not-A, so you naturally think it must be a member of the set {S,G}. But it isn't.


Now consider this:

1. Alphago is based on neural nets
2. http://www.webofstories.com/play/marvin.minsky/121

1+2 = 3: Alphago is a bottom-up approach to AI

4. GnuGo is a bottom-up approach to AI

3+4 = ?

5. Alphago uses convolution patterns to create candidate moves
6. GnuGo uses stone patterns to create candidate moves

5+6 = ?

7. Alphago's Monte-Carlo does treesearch (but it's not minimax)
8. Commonsense Go's metamethod does treesearch (but it's not minimax)

7+8 = ?


To begin to see more clearly, it can be helpful to study the basics:

http://ocw.mit.edu/courses/electrical-e ... t-systems/

and then read these:

Sacerdoti E. D., Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence, Vol . 5 , No. 2, pp 115 - 135, 1974.

Brown, DJH. Hierarchical Reasoning and the Game of Go. Proc Sixth International Joint Conference on Artificial Intelligence, Tokyo, 114-116, 1979.
https://books.google.com.au/books?id=VD ... &lpg=PA116

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 Post subject: Re: Commonsense Go
Post #27 Posted: Sat Aug 20, 2016 5:08 pm 
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djhbrown wrote:
History is full of such misunderstandings, and megalomaniacal psychopaths blinded by faith will even kill or excommunicate to sequester territory to protect their hyperactive egos.

/thread

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 Post subject: Re: Commonsense Go
Post #28 Posted: Sun Aug 21, 2016 6:06 am 
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wineandgolover wrote:
Satorian wrote:
Let's go really high level approach here and just boil this down to a winGame() function. Now it's everybody else's fault this hasn't been implemented yet.


I've been trying to write this function for years, but sadly have merely been perfecting blunder().


That's at least a function of yours we have learned something from. :)

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 Post subject: Re: Commonsense Go
Post #29 Posted: Mon Aug 29, 2016 8:39 pm 
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Playing Go is like going on a journey. You know where you are (here = the current position) and you know where you want to get (there = a won position). You don't know where there is, but you will recognise it when you see it. Your problem is to figure out how to get from here to there.

AI research identifies two basic problem-solving methods, or types of policy, called “generate-and-test” and “problem reduction” [3].

Generate-and-test is a reflex reaction. It matches here against a set of remembered <pattern, move> pairs and proposes the moves whose patterns match. For example, Alphago's policy network [1] generates move probabilities based on learned patterns represented by neural net convolution weights. A biologicial analogue of generate-and-test is bacterial chemotaxis, by which bacteria navigate chemical gradients towards attractive concentrations and away from repulsive ones.

Problem reduction is a commonsense way to go about solving a hard problem, by dividing it into two smaller and easier ones, as recommended by Polya's “How to Solve it” [15]. An everyday application of problem reduction is travel planning: “My goal is to get to there. i believe island is closer to there than here, so i will set myself two subgoals: first, get from here to island, and then get from island to there”. Examples of islands are conveyances such as cars, buses, and aeroplanes. It would be foolish to begin a long journey - even an imaginary one - by stepping out without some kind of overall plan.

Commonsense Go [2] uses a combination of problem reduction and generate-and-test. Problem reduction is used to create intermediate goal islands and to find move sequences that achieve the goal(s), assuming the opponent is trying to counter them. But since the responses of the opponent are not predictable, minimax and Monte-Carlo lookahead searches are used to test the overall utility of move candidates.

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 Post subject: Re: Commonsense Go
Post #30 Posted: Thu Feb 02, 2017 10:03 pm 
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Humans have been surpassed by machines in almost every endeavour - from swimming underwater to flying to bulldozing soil and bulldozing bytes.

The recent successes of Alphago playing under the modest pseudonym of Master(P) would seem to suggest that it's game over for even the best human players.

But does that mean Alphago is the best a Go player can ever be?

Most certainly not.

Presumably Master can comfortably beat the Alphago that beat Lee Sedol, and presumably some of the cracks in Alpha's design that Lee opened have been ironed out, but if Master is still using DCNN as her move generator and position evaluator, there is every possibility that a more reliable and accurate generator and evaluator can be engineered using a different computational technique.

Unquestionably, that technique will have to be whole board oriented; it cannot rely on local patterns. It will have to drive a smart searcher; one smarter than dice-tossing Monte, and that will require some fancy hardware parallelisation.

HALy aka HoLy aka Commonsense Go aka SWIM never grew beyond being a beam in the eye of the author (too old to care any more and too stupid to know what to do anyway), but one day, someone else might give birth to a son of SWIM who can knock Master off her bulldozer.

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Post #31 Posted: Fri Feb 03, 2017 9:29 am 
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AlphaGo upended 5000 years of human dominance less than a year ago. It doesn't seem like the time to lament that a theoretically much better go program hasn't yet been implemented.

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 Post subject: Re: Commonsense Go
Post #32 Posted: Fri Feb 03, 2017 11:00 am 
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Uberdude wrote:
Let's make this simple:

GnuGo uses the concept of groups in its design.

Commonsense Go uses the concept of groups in its design.

AlphaGo does not use the concept of groups in its design.

(and just in case AlphaGo does have groups in there somewhere, although not as core part):
The Go AI at https://chrisc36.github.io/deep-go/ is a pure neural net move selector bot (no MCTS or value network as in AlphaGo) so definitely doesn't have groups in its design.


Actually AlphaGo uses the concept of groups in its design at a very fundamental level: the board representation which is the input to the whole system uses a layer that tells what stones are connected in groups. This is described in the Nature paper.

Your intuition is right though - they could in principle have used just the stone positions - but that would take longer and be a more expensive learning process, since it would have to learn about the stones connectivity as well.

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 Post subject: Re: Commonsense Go
Post #33 Posted: Fri Feb 03, 2017 12:32 pm 
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djhbrown wrote:

Problem reduction is a commonsense way to go about solving a hard problem, by dividing it into two smaller and easier ones, as recommended by Polya's “How to Solve it” [15].


You just aren't "seeing it". Perhaps the reason being that not having enough practical experience in problem solving using software.

Solving the problem (play a very strong game of go) by using a neural net IS precisely an example of "finding the right island". And notice that I put it THAT way, because no, tackling a complex problem by just looking for any "island" between you and the goal is NOT a good way to proceed (Polya has oversimplified the arguement -- you need the right island, a useful island). Often a more fruitful way is to think of working backward from the goal (can you there would be a way from there to an island that was "terra cognito" (you already know how to get to THAT one).

Here the problem has broken into two parts. Code a program that implements a neural net and then train that net to evaluate the desired function <given a board position, return the best next move>. Both parts known how to do <if for board positions you can determine if the move the NN returns is the right one, then you can train the NN to evaluate the function>

And you aren't making a good case, not really making any case, that if your CS proposal can explain WHY that move is a good move the move would be a better move (than the SAME move sans explanation). Please, I am NOT telling you that being able to say "why" would not have value. Not saying that a program that made the right move AND told you why would not be a better program in the sense of more useful. But NOT better in the sense of making better moves (not a stronger go player).


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Post #34 Posted: Fri Feb 03, 2017 4:16 pm 
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Mike Novack wrote:

using a neural net IS precisely an example of "finding the right island".


yes, it is. it's a good example of humans applying Polya's principle to solve their problem.

i'm not talking about that; i'm talking about software itself using the principle to solve its own problem.

Mike Novack wrote:
You just aren't "seeing it". Perhaps the reason being that not having enough practical experience in problem solving using software.


please google me and check out my cv before defaming my PhD in artificial intelligence and other published work in software development.

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 Post subject: Re: Commonsense Go
Post #35 Posted: Sat Feb 04, 2017 9:34 pm 
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djhbrown wrote:

please google me and check out my cv before defaming my PhD in artificial intelligence and other published work in software development.



I did say PERHAPS. There could be other reasons. I can think of many possible reasons.

Never went back for a graduate degree but didn't stop studying and a few hundred thousand lines of code in my day. But that is NOT relevant.

But please do look at the LOGIC of what you are saying. If there is a function, which given a board position returns the best next move and there is a second function, which given a board position returns the pair of values, the best next move and what color your underwear is (pick anything) WHY do you say the second function would play stronger go?

Or how about just sticking to humans. Are you saying I should consider a top Chinese pro who does not speak English (and so cannot tell me why) would because of that necessarily play weaker go than a top Chinese pro who did speak English (and so could tell me why).

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Post #36 Posted: Sat Feb 04, 2017 11:59 pm 
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Mike Novack wrote:
...the LOGIC of what you are saying. If there is a function, which given a board position returns the best next move and there is a second function, which given a board position returns the pair of values, the best next move and what color your underwear is (pick anything) WHY do you say the second function would play stronger go?


i didn't say that.

i couldn't decide whether you genuinely don't follow the logic of my argument, or are just winding me up (ie, trolling).

So i googled you but could only find your comments on other topics in this forum, which led me to believe that i should give you the benefit of the doubt and assume it is the former case.

So here is my attempt to explain my argument:

Master Alphago's technique is the best we know of, so far. But that doesn't mean she is the best that could ever be made, even if no human could ever beat her.

So the questions facing everyone interested in AI are these:

1. what could be better?
2. what does DCNN+Monte's success tell us about the intellectual challenge of Go?

last year i mused upon the second question in comments on YouTube videos of the Alphago - Lee Sedol match.

As to the first question, i don't have an unequivocal answer, but i did make a suggestion (CG) for future research last year, which included testing a simulated CG on just one example - the only example i know of where Alphago made a game-losing move. I was pleasantly surprised that CG quickly found a move that as far as i could see was better than the move Alphago made - it was a variation of the defence to Lee's wedge that Myungwan Kim found.

By itself, that doesn't prove anything, but it is a suggestive evidence.

Since then, DeepMind have said that they have improved Alphago so she wouldn't make the same mistake again in the same situation.

So nothing can be said for sure as to the relative playing strengths of DCNN and deductive logic. It would, however, be easy to test: simply replace Master's DCNN move generator with CG's, and her evaluation DCNN with CG's estimation function, and using all the rest of Master's paraphernalia (parallel Monte search on gerzillions of processors), see what happens when they face each other.

Only DeepMind could do that test, but a pimply kid with a PC could do a simpler and cheaper mini-test: implement Alpha's published algorithms as one program and CG's as another and let them battle it out on the same hardware. My adolescence finished half a century ago, and the rest of my life is too short, so i won't be that pimply kid.

As to the fact that CG can SWIM, i never claimed that that implies its thinking is superior to any other technique - that was your own illogical inference.

That it can, merely demonstrates that its way of thinking is comprehensible to people in terms that they can understand. If its thinking is reasonable, then it could be a useful learning aide.

Please note that i am using the term "thinking" in its Turing sense; i do not claim that CG's thinking is isomorphic to human thinking. Indeed, i have sometimes tried to apply CG's methods in my own games, and have sometimes discovered a move that was better than the one which had first come to my mind. But i am a weak player, so that doesn't mean anything - it's merely an indoor recreation.


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 Post subject: Re: Commonsense Go
Post #37 Posted: Sun Feb 05, 2017 1:20 am 
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So the conclusion is that djhbrown

-never claim his suggestion is superior to other suggestion

I agree

- his suggestion can satisfied his curiosity

I agree

-never claim his suggestion can benefit to any people other than him

I agree

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Post #38 Posted: Sun Feb 05, 2017 1:27 am 
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djhbrown wrote:
The program is designed, but it's up to others to turn it into software. Could it beat Alphago across the board, and with much less hardware? I think it could


This is unlikely that somebody will invest energy and time to implement somebody else ideas if that person is not convinced by your ideas from the beginning.

But, really, why don't you just go ahead an make that software yourself? I means, whatever how old you are now, considering your academic background, learning programming up to a sufficient level to implement your ideas is probably a matter of 1~3 months of self teaching on your spare time.

As a side effect, you may very well get rich in the process, or get a Nobel Prize, or land the perfect job at some perfect company.

Best regards,

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Post #39 Posted: Sun Feb 05, 2017 8:55 am 
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No, I am not "trolling" but yes, I do not get your logic.

I wish you would take another look at what you are saying in computer science terms, in mathematical and logical terms.

I will now more carefully/precisely define a FUNCTION. The function shall be "given a board position*, return a move such that there is no better move" < I have changed that from "best move" because there might not BE a best move. That this would sometimes be the case should be obvious from symmetry > So this function would always return a value, not necessarily unique, and so not always the same value every time evaluated.

I have NOT yet discussed HOW the eval will be done. Not talking about implementation in specific terms but in general terms.

Now assume we have two (very different) computer programs, A and B that both correctly "eval" that function. WHY (for what reason) are you arguing B is better than A? Having done a lot of this sort of thing, I can understand reasons like "runs faster", "easier to test", etc. But I cannot understand "more correctly" because we began assuming that they both DID correctly evaluate the function.

Look, back in my working days, given a problem that I saw could be solved (at the abstract level) by a finite state machine I might choose that approach (define the FSM and then write a program that implemented all the states/transitions). I might choose that because that "island" broke the problem into some very well defined and easily testable parts (independently testable). Almost all the of the other programmers in this very large shop would have tackled the problem more directly. But once they had gotten their solution debugged I would not make a claim like "my solution was more CORRECT".

Treat me like I was dense. That I am somehow not able to see that a program (in the ABSTRACT sense) depended on what language was used to create the concrete implementation.

Again to look at this from the human side of things, what exactly makes you conclude that the ability of the top pro to look at a proposed move and explain why it was a good move (or do the reading to confirm a good move) meant that this is how (the mental process) by which the move was discovered. When going over the course of a game by two top human players, doesn't one of them sometimes make a move that the other did not expect? Though afterwards can see why a good move.

If you were making a claim like "the process of training a neural net to the function will not converge fast enough to a correct evaluation of the function" so we need some other solution I would be more understanding << that te training of a large enough neural net must converge on a correct solution is clear; though the domain is VERY large, it IS finite >>

* By "board position" I don't mean just the stones on the board but also some "state information", for example, whether any point illegal because of ko, etc.


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Post #40 Posted: Sun Feb 05, 2017 5:15 pm 
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Mike Novack wrote:
I will now more carefully/precisely define a FUNCTION. The function shall be "given a board position*, return a move such that there is no better move" ...

Now assume we have two (very different) computer programs, A and B that both correctly "eval" that function. WHY (for what reason) are you arguing B is better than A?


as the Irishman said: "If I were going to Dublin, I wouldn't be starting from here".

Mike, i think you are tying yourself in knots because you are starting off from the wrong place, namely: you seem to have inferred that i assume that A and B both compute a function of a board position which "return a move such that there is no better move".

But i don't assume that.

i do assume this:

Let A = Alphago and B = CG and K = Myungwan Kim.
Let p = the position after move 78 in game 4 of Lee Sedol vs Alphago
Then A(p) = 79: K10, .. (DeepMind have never told us what followup move tree Alphago had expected)
, and B(p) = 79: J13, L12; 80: L10, .. (see https://papers.ssrn.com/sol3/papers.cfm ... id=2818149)
, and K(p) = 79: L10, etc (see sgf)

1. We know from the game record that K10 didn't work, because Lee saved his dead stones and went on to win the game.
2. Kim showed us that L10 does work
3. i believe that J13 is better than K10, because i think that J13 forces L12, after which black can play L10 and kill white in a variation of Kim's analysis.
Attachment:
game4.sgf [17.3 KiB]
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game4move78.png [ 370.36 KiB | Viewed 743 times ]

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