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### AuthorTopic: Letting a computer design Dominion cards: recurrent neural network edition  (Read 4941 times)

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#### Kirian

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #50 on: February 03, 2018, 05:13:50 pm »
+3

Echip
Action–Duration|\$2
+e Cards.
+1 Action.

Strictly better than Laboratory

But requires you to destroy some cards *and* make good estimates of what 0.718 of a card is.
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#### Awaclus

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #51 on: February 03, 2018, 05:49:46 pm »
0

I don't think that would bring much to the table in practice.

Firstly, as we've discovered, even constructing meaningful cards is difficult. Secondly, as we've discovered, making a computer play Dominion well is difficult. It's not easy to be in a position to have a computer design cards and playtest them.

Well, that might not be the case. What we're doing right now is trying to construct meaningful cards from a scratch in human language, character by character, which we have known is a bad idea since day one, and we have expectably discovered that it's difficult. The computer could be doing something exponentially more efficient, for example if it knew and understood the rules and mechanics of Dominion to begin with.

Making a computer play Dominion well is a very nuanced problem, because what qualifies as "playing Dominion well" highly depends on what you're trying to achieve. When you're saying that we've discovered that making a computer play Dominion well is difficult, I think you're referring to the Goko and Shuffle iT bots, and possibly things like Geronimoo's sim and Dominiate. However, both simulators and Dominion Online bots have certain very ambitious goals that our hypothetical AI doesn't need, because we're actually fine if the bot just plays a reasonably small number (a few dozen, maybe a few hundred if we're greedy) of arbitrary kingdoms really well. And we have, in fact, discovered that that's possible and successfully used that to evaluate the power level of cards.

What Philipp is suggesting doesn't sound ridiculous to me. It's just that you can't simply throw a 2009 IBM server machine at the problem and expect it to solve it by itself.
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#### crj

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #52 on: February 03, 2018, 06:23:46 pm »
0

What Philipp is suggesting doesn't sound ridiculous to me. It's just that you can't simply throw a 2009 IBM server machine at the problem and expect it to solve it by itself.
Conversely, I can easily write this Python program:
Code: [Select]
`print """RatsAction, cost \$4+1 Card+1 ActionGain a Rats. Trash a card from your hand other than a Rats (or reveal a hand of all Rats)._When you trash this, +1 Card."""`So now I've written a computer program that produces good Dominion cards.

Right?

To put it another way, there is an important distinction between machine learning, and encapsulating human learning in a computer program. There's a grey area between the two, but it does feel more legitimate to train a neural network on example cards and non-cards, rather than hand-hold it with "the rules and mechanics of Dominion to begin with".
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#### Awaclus

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #53 on: February 03, 2018, 09:12:21 pm »
+2

To put it another way, there is an important distinction between machine learning, and encapsulating human learning in a computer program. There's a grey area between the two, but it does feel more legitimate to train a neural network on example cards and non-cards, rather than hand-hold it with "the rules and mechanics of Dominion to begin with".

It's not human learning, it's not even on the grey area. The machine has to recognize something to begin with, otherwise there's no way it can learn anything. Right now, the thing it recognizes is individual characters in a text file. If it recognized Dominion mechanics instead, that would be the same thing except a lot less stupid.

Really, the problem with the current implementation is that the vast majority of stuff it creates is like this (actual thing it created):

Sile Gail Haons ona
Action
Each other player reveals cards from the top of choosese. Gain a Treasure card up to 3 Duchose.

There are so many things the machine had to learn in order to come this far. It had to learn how to spell all those English words. It had to learn that cards have a name, a type, a cost, an ability, and a line break to signify that the card is over and a new card is about to start. It had to learn that is a cost. It had to learn that "Action" is a type. It had to learn that Treasure cards can be gained, and that revealing cards from the top is something that each other player can do. However, this card is completely useless, it doesn't even mean anything. The machine doesn't even get to the point where it can combine Dominion mechanics because it still fails the point at which it's supposed to combine individual characters to spell words correctly and form coherent sentences.
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#### faust

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #54 on: February 04, 2018, 01:58:41 am »
+1

Secondly, as we've discovered, making a computer play Dominion well is difficult.

That's what they said about Go too, then Google DeepMind came along

Are you suggesting that creating Google DeepMind was not a difficult task?
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#### GendoIkari

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #55 on: February 04, 2018, 08:51:56 am »
0

To put it another way, there is an important distinction between machine learning, and encapsulating human learning in a computer program. There's a grey area between the two, but it does feel more legitimate to train a neural network on example cards and non-cards, rather than hand-hold it with "the rules and mechanics of Dominion to begin with".

It's not human learning, it's not even on the grey area. The machine has to recognize something to begin with, otherwise there's no way it can learn anything. Right now, the thing it recognizes is individual characters in a text file. If it recognized Dominion mechanics instead, that would be the same thing except a lot less stupid.

Really, the problem with the current implementation is that the vast majority of stuff it creates is like this (actual thing it created):

Sile Gail Haons ona
Action
Each other player reveals cards from the top of choosese. Gain a Treasure card up to 3 Duchose.

There are so many things the machine had to learn in order to come this far. It had to learn how to spell all those English words. It had to learn that cards have a name, a type, a cost, an ability, and a line break to signify that the card is over and a new card is about to start. It had to learn that is a cost. It had to learn that "Action" is a type. It had to learn that Treasure cards can be gained, and that revealing cards from the top is something that each other player can do. However, this card is completely useless, it doesn't even mean anything. The machine doesn't even get to the point where it can combine Dominion mechanics because it still fails the point at which it's supposed to combine individual characters to spell words correctly and form coherent sentences.

Actually one of the main weaknesses with all of these cards is that the machine did not ever learn most of the things you just said. It doesn’t know that action is a type. It just knows that a lot of real cards put the word “action” on the second line of text. It doesn’t know that that icon is a cost; it just knows that some real cards put that icon on the second line. It doesn’t know that treasure cards can be gained; it just knows that “gain a treasure” is a set of words that sometimes appear together in that order. It never learned what any of those things actually mean.
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#### Awaclus

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #56 on: February 04, 2018, 09:39:37 am »
+1

To put it another way, there is an important distinction between machine learning, and encapsulating human learning in a computer program. There's a grey area between the two, but it does feel more legitimate to train a neural network on example cards and non-cards, rather than hand-hold it with "the rules and mechanics of Dominion to begin with".

It's not human learning, it's not even on the grey area. The machine has to recognize something to begin with, otherwise there's no way it can learn anything. Right now, the thing it recognizes is individual characters in a text file. If it recognized Dominion mechanics instead, that would be the same thing except a lot less stupid.

Really, the problem with the current implementation is that the vast majority of stuff it creates is like this (actual thing it created):

Sile Gail Haons ona
Action
Each other player reveals cards from the top of choosese. Gain a Treasure card up to 3 Duchose.

There are so many things the machine had to learn in order to come this far. It had to learn how to spell all those English words. It had to learn that cards have a name, a type, a cost, an ability, and a line break to signify that the card is over and a new card is about to start. It had to learn that is a cost. It had to learn that "Action" is a type. It had to learn that Treasure cards can be gained, and that revealing cards from the top is something that each other player can do. However, this card is completely useless, it doesn't even mean anything. The machine doesn't even get to the point where it can combine Dominion mechanics because it still fails the point at which it's supposed to combine individual characters to spell words correctly and form coherent sentences.

Actually one of the main weaknesses with all of these cards is that the machine did not ever learn most of the things you just said. It doesn’t know that action is a type. It just knows that a lot of real cards put the word “action” on the second line of text. It doesn’t know that that icon is a cost; it just knows that some real cards put that icon on the second line. It doesn’t know that treasure cards can be gained; it just knows that “gain a treasure” is a set of words that sometimes appear together in that order. It never learned what any of those things actually mean.

Well, it hasn't learned anything at all. It doesn't have any idea what it's doing, it's just a non-sentient machine. Its efforts only become "knowledge" or "learning" when we interpret them in the light of what we know and understand. If it evolves to put the characters "A", "c", "t", "i", "o" and "n" in that order in the slot where real cards have a card type, we know that it means it has designed an Action card — therefore, it has learned how to do that.
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#### GendoIkari

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #57 on: February 04, 2018, 10:09:35 am »
0

To put it another way, there is an important distinction between machine learning, and encapsulating human learning in a computer program. There's a grey area between the two, but it does feel more legitimate to train a neural network on example cards and non-cards, rather than hand-hold it with "the rules and mechanics of Dominion to begin with".

It's not human learning, it's not even on the grey area. The machine has to recognize something to begin with, otherwise there's no way it can learn anything. Right now, the thing it recognizes is individual characters in a text file. If it recognized Dominion mechanics instead, that would be the same thing except a lot less stupid.

Really, the problem with the current implementation is that the vast majority of stuff it creates is like this (actual thing it created):

Sile Gail Haons ona
Action
Each other player reveals cards from the top of choosese. Gain a Treasure card up to 3 Duchose.

There are so many things the machine had to learn in order to come this far. It had to learn how to spell all those English words. It had to learn that cards have a name, a type, a cost, an ability, and a line break to signify that the card is over and a new card is about to start. It had to learn that is a cost. It had to learn that "Action" is a type. It had to learn that Treasure cards can be gained, and that revealing cards from the top is something that each other player can do. However, this card is completely useless, it doesn't even mean anything. The machine doesn't even get to the point where it can combine Dominion mechanics because it still fails the point at which it's supposed to combine individual characters to spell words correctly and form coherent sentences.

Actually one of the main weaknesses with all of these cards is that the machine did not ever learn most of the things you just said. It doesn’t know that action is a type. It just knows that a lot of real cards put the word “action” on the second line of text. It doesn’t know that that icon is a cost; it just knows that some real cards put that icon on the second line. It doesn’t know that treasure cards can be gained; it just knows that “gain a treasure” is a set of words that sometimes appear together in that order. It never learned what any of those things actually mean.

Well, it hasn't learned anything at all. It doesn't have any idea what it's doing, it's just a non-sentient machine. Its efforts only become "knowledge" or "learning" when we interpret them in the light of what we know and understand. If it evolves to put the characters "A", "c", "t", "i", "o" and "n" in that order in the slot where real cards have a card type, we know that it means it has designed an Action card — therefore, it has learned how to do that.

Right, I’m just saying that it is at least theoretically possible for a machine to design cards based not only off of similarities to existing cards, but on the rules and concepts of Dominion itself. It’s just way harder and needs a much more advanced computer.
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#### Awaclus

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #58 on: February 04, 2018, 10:20:39 am »
0

Right, I’m just saying that it is at least theoretically possible for a machine to design cards based not only off of similarities to existing cards, but on the rules and concepts of Dominion itself. It’s just way harder and needs a much more advanced computer.

Either way it's going to need to "study" the similarities between existing cards in order to achieve anything. It's just that the smallest thing that it treats as a single unit right now is an individual character, whereas it could be a Dominion concept. It doesn't even need a more advanced computer, it only really needs a more advanced data set and an algorithm which is sufficiently advanced to parse that data set in the intended way.
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#### Philipp

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #59 on: February 04, 2018, 10:27:31 am »
+2

Are you suggesting that creating Google DeepMind was not a difficult task?

Yes, it was incredibly hard to lay the foundation the first time. So was writing the first C compiler. It then becomes expontentially more easy when that foundation is released to the public in the form of libraries, which is what happens all the time in the deep learning and programming community. One machine learning open source framework is Google TensorFlow, for example. Based on that, other frameworks popped up, like the amazing Pix2pix, which can be trained with sample data to generate image sets. And if you google DeepFakes, you'll find a lot of NSFW examples of what people are doing on their home computers today with little experience... replacing faces in movies through a deep learning network! Amazing stuff where we can be dwarves on the shoulders of giants, but where our absolute height is still increasing.

There's a grey area between the two, but it does feel more legitimate to train a neural network on example cards and non-cards, rather than hand-hold it with "the rules and mechanics of Dominion to begin with".

Yes, definitely a gray area. But every approach will choose some layer of abstraction. Even AlphaGo had some form of rule-based pre-processing before it send things to the neural network. The "understanding" of a machine-learning "AlphaDominion" could be as simple as knowing the current properties & rules and having them available in text data format, it wouldn't even need an iota of strategy input. *Any other format* you deliver to the neural network too is a human-chosen abstraction of some sort, even if it was a scan of the card in pixel format (you had to settle for a certain resolution; you sneakily photographed the card from the front because you as human know that gives it more meaning; you defined what is a Dominion card and didn't photograph the bottom of the box; you had proper lighting in the shot, etc. -- these are all steps to ensure that basically the text on the card is readable, short of actually transcribing it, but that would be just another step).

Most of these abstractions (which require human knowledge) are in the strict sense just there to save computing time. If the computer was infinitely fast, then you could have it simulate the whole universe and its atoms, and tell it to evolve over billions of simulation years to come up with a planet where someone invents a card game which then gains good reviews on the simulated Amazon. And who knows, we might be the ones living in such a simulation...
« Last Edit: February 04, 2018, 10:29:03 am by Philipp »
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#### Commodore Chuckles

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #60 on: February 04, 2018, 12:13:54 pm »
+2

Sile Gail Haons ona

This looks very Celtic. I don't know why I felt like pointing that out, I just thought it was interesting.
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#### Donald X.

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #61 on: February 04, 2018, 02:19:20 pm »
+5

Right, I’m just saying that it is at least theoretically possible for a machine to design cards based not only off of similarities to existing cards, but on the rules and concepts of Dominion itself. It’s just way harder and needs a much more advanced computer.
Maybe what you're imagining does, but it's way way easier to piece together meaningful cards using all existing Dominion concepts, than it is to write a program that "learns." It won't come up with anything novel, but then neither will the learning program.

I put work into this 20 years ago on Magic. Other people have done it since and you can go see the output of one of these programs somewhere. You just consider all of the different forms a rule can take, and all of the smallest pieces the game provides, and then randomly pick stuff. For example you need to pick an effect. One meta-effect is "Effect. Effect" - do two things. You pick that. For the first effect you pick "+N Cards." N could be a formula, any of the things you can count, but it can also just be a number, and you pick 2, to keep this paragraph short. For the second effect you pick "Gain a [card]." Then you have to pick what "[card]" is, there are all the ways you can qualify a card. We pick the choose-one meta thing and go with "[card] or [card]." For the first one we pick "costing \$N" and have to pick N, and this time pick a formula, to make this paragraph longer. The formula is "number of cards in your hand." For the other "[card]" we also happen to pick costing \$N, but this time N is 2, okay. So "+2 Cards. Gain a card costing \$1 per card in your hand or a card costing \$2."

The output is not fascinating, as you can see with the Magic cards. It's like that joke in Dirk Gently, the book, where he constrains the cacophony of notes to C and gets cacophony in C. Since we picked randomly, we got random stuff. If you look through them you can maybe find a nice one, but it isn't so much that the program produced a nice one, as it is that your brain produced it, through a slow method where you look at lots of random stuff and evaluate it. If you looked at every producible random card you'd spot all the good ones. You know? There's this logic for making the cards better that your brain knows but the program doesn't.

So then you want that logic. That was too much work 20 years ago and I don't think anyone else has done it since. I always imagined the Magic program producing things like, "Sacrifice a blue artifact: Until end of turn, creatures you control are untapped." On an Enchant Creature spell with a cost of XG. There are all these things about how Magic works that aren't just in the items you randomly pick; artifacts aren't ever blue (it's been many years and at this point a few expansions have had blue artifacts), being untapped isn't the kind of thing that can have a time period for it, an Enchant Creature should refer to the enchanted creature somehow; an X spell should use the X somewhere. But those rules are just part of it; like, if you have "Effect. Effect," well you would like that pairing to make some kind of sense, not to just be two random things, and you have to figure out, what do you mean by that, what makes sense.

There is however one basic thing you can get out of a program like this, sans the tricky logic, which is, it can help you find areas of possibilities you weren't using. You make the list of triggers for "trigger -> effect," and think of one you haven't done yet.
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#### crj

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #62 on: February 04, 2018, 02:36:22 pm »
0

It's like that joke in Dirk Gently, the book, where he constrains the cacophony of notes to C and gets cacophony in C.
Yes! That!

(I almost mentioned it myself. Now I'm wishing I had. (-8 )
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#### King Leon

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #63 on: February 04, 2018, 07:28:09 pm »
+2

What Donald is talking about, is a context-free grammar (CFG). A good way to generate cards with this approach would be genetic programming. You can split existing cards into their terminal and nonterminal symbols and recombine them. Then you can evaluate the balancedness of a card if you playtest it (this can be an automated cpu vs cpu game). As higher a card’s balancedness is, as higher is the probability that it is used for the next iteration of recombination (to prevent genetic extinction this chance is never zero, even for very bad or uber cards). There is also a small chance for mutations, which randomly changes a value to create new genetic material. I think this can work well.
« Last Edit: February 04, 2018, 07:37:46 pm by King Leon »
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#### crj

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #64 on: February 05, 2018, 12:05:49 pm »
0

It could work well except that what we started out looking for in this thread was hilarity, and what we seem to have drifted into wanting is a Donald-o-matic that can spit out new Dominion expansions for us with quality at least as good as those made by the real Donald. If the Donald-o-matic managed to work on hardware that cost less to run than the real Donald, that would suddenly move from the realms of an academic exercise to another scary step towards the technological singularity.

What I don't think anybody really wants is a computer which learns how to make balanced cards very similar to those already in the game.

A genetic algorithm running on sentences in a CFG isn't going to come up with Durations, VP tokens, coin tokens, Reserves, split piles, Debt, Night, etc. having seen only the base game.
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#### crj

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #65 on: February 05, 2018, 12:11:42 pm »
0

Postscript:

I've just realised a genetic algorithm could potentially come up with good answers given briefs like: "This is a proposed new Night mechanic. Make some balanced cards which use it." or "This Night card costs \$1 and says 'At the start of your next turn, +4 Cards. This is gained to your hand (instead of your discard pile).' It's interesting; now please make it balanced."

And that's probably the most promising line of attack for using computers to help design real Dominion cards.
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#### ThetaSigma12

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #66 on: February 05, 2018, 12:13:59 pm »
+13

i was just here for the hilarious dominion cards and now its a debate on ai can we go back to funny cards plz
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#### Gazbag

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #67 on: February 05, 2018, 01:02:28 pm »
+14

i was just here for the hilarious dominion cards and now its a debate on ai can we go back to funny cards plz

I just got this funny Patrol variant:

Scout
Action
\$4
+1 Action
Reveal the top 4 cards of your deck. Put the revealed Victory cards into your hand. Put the other cards on top of your deck in any order.
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#### King Leon

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #68 on: February 05, 2018, 03:45:42 pm »
0

A genetic algorithm running on sentences in a CFG isn't going to come up with Durations, VP tokens, coin tokens, Reserves, split piles, Debt, Night, etc. having seen only the base game.
Potion and Dept are just mutations of money, Night is a mutation of Action, Reserve is a mutation of Island Mat, which is a mutation of trash, etc.
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#### Commodore Chuckles

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #69 on: February 05, 2018, 11:06:14 pm »
+3

i was just here for the hilarious dominion cards and now its a debate on ai can we go back to funny cards plz

I just got this funny Patrol variant:

Scout
Action
\$4
+1 Action
Reveal the top 4 cards of your deck. Put the revealed Victory cards into your hand. Put the other cards on top of your deck in any order.

I got a weird one too:

Bomb
Action
\$1G
Trash this and a card from your hand.
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#### faust

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #70 on: February 06, 2018, 04:30:10 am »
0

you'll find a lot of NSFW examples of what people are doing on their home computers today with little experience... replacing faces in movies through a deep learning network! Amazing stuff
I beg to differ.
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#### Rabid

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #71 on: February 06, 2018, 05:05:51 am »
+2

you'll find a lot of NSFW examples of what people are doing on their home computers today with little experience... replacing faces in movies through a deep learning network! Amazing stuff
I beg to differ.

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#### Dingan

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #72 on: February 06, 2018, 12:19:12 pm »
0

Echip
Action–Duration|\$2
+e Cards.
+1 Action.

Strictly better than Laboratory

But requires you to destroy some cards *and* make good estimates of what 0.718 of a card is.

Toss up your thousand Echip tokens. If 718 or more of them landed heads-up, draw a card.
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#### sudgy

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #73 on: February 06, 2018, 12:49:23 pm »
0

Echip
Action–Duration|\$2
+e Cards.
+1 Action.

Strictly better than Laboratory

But requires you to destroy some cards *and* make good estimates of what 0.718 of a card is.

Toss up your thousand Echip tokens. If 718 or more of them landed heads-up, draw a card.

But...that's not e cards.  That's just 2.718 cards.

How about this: Toss up your ten Echip tokens.  If less than 7 landed heads-up, draw a card.  If exactly 7 landed heads-up, repeat this process, but with 1, then 8, then 2, then 8, and so on...
« Last Edit: February 06, 2018, 12:51:03 pm by sudgy »
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#### crj

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##### Re: Letting a computer design Dominion cards: recurrent neural network edition
« Reply #74 on: February 06, 2018, 01:07:24 pm »
+4

Um. Unless an "Echip token" is different from a coin in some way, both these are bogus.

If you toss 1,000 coins, the chance fewer than 718 of them coming up heads isn't 0.718 . (Let alone the chance of 718 or more coming up heads.) Similarly, if you toss 10 coins, the chance of fewer than 7 of them coming up heads isn't 0.7 . (My calculator says 0.99995 or so!)

The way to do this would be to use the binary expansion of e and toss a single coin until the result is clear.
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