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When they say “Constructed” is expensive because you have to acquire the cards ahead of time... why don’t tournaments allow you to play with cards where you’ve just scribbled with Sharpie whatever card you want it to act as?
>> Using data from drafts carried out by humans, I trained a neural network to predict what card the humans would take out of each pack. It reached 60% accuracy at this task.
Going by what's in the linked notebook, the model was evaluated on its ability to match the decks in its training set card-for-card.
Without any attempt to represent game semantics in the model, the fact that the deck sometimes "predicts" different picks than the actual picks in the dataset tells us nothing. It probably means the model has some variance that causes it to make "mistakes" in its attempt to exactly reproduce its dataset. It certainly doesn't say that the model can draft a good M:tG deck, certainly not in any set other than Guilds of Ravnica.
>> The model definitely understands the concept of color. In MTG there are 5 colors, and any given draft deck will likely only play cards from 2 or 3 of those colors. So if you’ve already taken a blue card, you should be more likely to take blue cards in future picks. We didn’t tell the model about this, and we also didn’t tell it which cards were which color. But it learned anyway, by observing which cards were often drafted in combination with each other.
This is a breathakingly brash misinterpretation of the evidence. The model's representation of a M:tG card is its index in the Guilds of Ravnica card set. It has no representation of any card characteristic, including colour. If it had learned to represent "the concept of colour" in M:tg in this way, it wouldn't be a neural net, it would be a magick spell.
The author suggests that the model "understands" colour because it drafts decks of specific colours. Well, its dataset consists of decks with cards of specific colours. It learned to reproduce those decks. It didn't learn anything about why those decks pick particular cards, or what particular cards are. All it has is a list of numbers that it has to learn to put together in specific ways.
This is as far from "understanding the concept of colour", or anything, as can be.
There are many more "holes" in the article's logic, that just go to show that you can train a neural net, but you can't do much with it unless you understand what you're doing.
Apologies to the author for the harsh critique, if he's reading this.
Given the information that this AI now has and the ability it's displayed, should WotC/Hasbro consider actively banning players from using AI to build standard-format Constructed decks? After all, if the author actually is correct and draftbot here picks better than humans, wouldn't it stand to reason that it could build optimal decks if given a "draft" which included multiple iterations of the program working in tan…
That is no way to evaluate the quality of an M:tG deck. For instance, it can never tell us anything about "sleeper" decks, or about the value to an existing deck of new cards that are added to a format as sets rotate and so on.
All that the accuracy metric used by the author can do is tell us how good the model is at representing the past. I am of the firm belief that WoTC will be laughing in their tea cups in the tought of banning something as pointless as this. In M:tG the past is about as valuable as a hat made of ice in the tropics.
Edit: For some added context. The way M:tG metagames work is that at the start of a season, there are some "decks to beat" that are usually the most obvious ones in the format. As the format progresses, players often find strategies to beat the decks to beat, initially known as "rogue" or sleepers. These can't be predicted by representing the current decks to beat. Some level of understanding of the game and what's important in a format in terms of strategy etc, is required.
Famous example. "The solution" by Zwi Mowshowitz [1] that dominated the 1st Pro Tour–Tokyo 2001 Invasion Block Constructed. Mowshowitz noticed that the dominant aggro decks' clocks (sources of damage) were predominantly red, so he stuffed a deck with anti-red cards, shutting down the dominant aggro decks.
That requires way, way more than modelling the current metagame at any point in time.
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[1] http://www.starcitygames.com/magic/misc/22483_Innovations_Th...
There is also more nuance in draft. For example, if you see that a particular color set is closes (i.e. people are drafting it), you might grab a particularly powerful card in that color set just to prevent them from obtaining it.
When they say “Constructed” is expensive because you have to acquire the cards ahead of time... why don’t tournaments allow you to play with cards where you’ve just scribbled with Sharpie whatever card you want it to act as?
It's like saying, "why do movie theaters pay so much for films? Why don't they just download the film from a BitTorrent like everyone else?"
Sorry but this is extremely shoddy work. M:tG will never be "solved" like this. >> Using data from drafts carried out by humans, I trained a neural network to predict what card the humans would take out of each pack. It reached 60% accuracy at this task. Going by what's in the linked notebook, the model was evaluated on its ability to match the decks in its training set card-for-card. Without any attempt to represent…
>> Using data from drafts carried out by humans, I trained a neural network to predict what card the humans would take out of each pack. It reached 60% accuracy at this task. And in the 40% when the human and the model differ, the model’s predicted choice is often better than what the human picked.
How the model's pick is "better than what the humn picked" is never made clear, but since accuracy is measured by the model's ability to match its training set, I assume that's also what is meant by "better": the model was better than a human in memorising and reproducing the decks it saw during training.
Well, you'd never evaluate a human's deckbuilding skills by how well they can reproduce a deck they've seen before. Given the same deck archetype, 10 humans will probably make 10 different card choices, for reasons of their own. It's like trying to evaluate how people style their hair by measuring how similar their hair looks to some examples of particular hair styles. It's a concrete measure, but it's also entirely meaningless.
This effort really suffers in terms of evaluation, and so we have learned nothing about how good the model is, which is a shame.
Sorry but this is extremely shoddy work. M:tG will never be "solved" like this. >> Using data from drafts carried out by humans, I trained a neural network to predict what card the humans would take out of each pack. It reached 60% accuracy at this task. Going by what's in the linked notebook, the model was evaluated on its ability to match the decks in its training set card-for-card. Without any attempt to represent…
> This is as far from "understanding the concept of colour", or anything, as can be.
It is very arguably bad feature engineering - if you have the information readily available, don't make the network infer it - but I think the description is fair.
Word2vec uses a similar model. It starts out knowing nothing about each word except an arbitrary numeric index, and learns everything else by predicting words that appear next to each other. By the end of the training it clearly has internal representations of concepts like "color", "verb", "gender", etc.
The same concept should apply here - by observing what cards are used in similar decks, with enough training data it should eventually associate concepts like card type, color and mana costs to each card.
In this case there isn't enough training data for that kind of resolution, but it has learned that blue cards go with blue cards, and red cards with red cards, and there's no hard lines from there to the concept of color.
Sure this isn't going to "solve" MtG, and I don't think it is a particularly good approach for the problem statement, but I think the idea is workable, and the network could already contain a proto-concept of "color" that would be refined with more training.
Sorry but this is extremely shoddy work. M:tG will never be "solved" like this. >> Using data from drafts carried out by humans, I trained a neural network to predict what card the humans would take out of each pack. It reached 60% accuracy at this task. Going by what's in the linked notebook, the model was evaluated on its ability to match the decks in its training set card-for-card. Without any attempt to represent…
> The author suggests that the model "understands" colour because it drafts decks of specific colours. Well, its dataset consists of decks with cards of specific colours. It learned to reproduce those decks. It didn't learn anything about why those decks pick particular cards, or what particular cards are. All it has is a list of numbers that it has to learn to put together in specific ways. > This is as far from "un…
Sorry but this is extremely shoddy work. M:tG will never be "solved" like this. >> Using data from drafts carried out by humans, I trained a neural network to predict what card the humans would take out of each pack. It reached 60% accuracy at this task. Going by what's in the linked notebook, the model was evaluated on its ability to match the decks in its training set card-for-card. Without any attempt to represent…
> The author suggests that the model "understands" colour because it drafts decks of specific colours. Well, its dataset consists of decks with cards of specific colours. It learned to reproduce those decks. It didn't learn anything about why those decks pick particular cards, or what particular cards are. All it has is a list of numbers that it has to learn to put together in specific ways. > This is as far from "un…
A card is blue (resp. red, etc) because it has a blue mana symbol in its casting cost. Not because it is found in the company of other blue cards. That is the concept of colour that a model must represent before you can say with any conviction that it "understands" the concept of colour. In terms of "hard lines"- that's the hard line you must cross.
The kind of model you're talking about then would be a classifier able to label individual cards with their colours, or an end-to-end model with an internal representation of cards' charactersitics. That is not what was shown here.