How would one approach developing an AI for a trading card game(e.g. Magic The Gathering, YuGiOh, etc.)?
I'm not sure where to even begin. How would "Easy", "Normal", and "Hard" AI difficulties differ under the hood?
closed as too broad by Anko, Krom Stern, bummzack, congusbongus, Seth Battin Mar 24 at 23:49
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This is my experience from implementing a MTG AI.
If the AI is not considered from the start, adding it later can be challenging.
Your engine needs a way to copy the game state. It also needs a way to to quickly undo a move, so search tree can be generated efficiently.
Every AI thread should operate on its own copy of the game.
When computer makes a move it simulates possible outcomes of the game by simulating the effects of every possible move.
The basic algorithm is:
Create a list of valid moves (spells, abilities, pass ...). Execute each move to generate a new state then create a new list of valid moves. Repeat this until the game ends, or until you reach a certain depth. Remember you simulate the game for both players.
This process will generate a tree. Each path on the tree represents a valid sequence of actions in the game. Assign a score to each leaf node of this tree. This score should be an indicator which player has a greater chance of winning the game. Using minimax algorithm propagate this score to the root of the tree.
Every move from the original list now has a score, pick the best move.
Unfortunately MTG is rather complicated and the basic algorithm will not perform very well...
Things to improve:
Here is a card implementation which uses some heuristics from above:
Magic: The Gathering Duels of the Planeswalkers has AI that simulates possible future game states. Different AI difficulties are allowed varying lengths of time to plan advance turns. Additionally, the decks of easier AI opponents are sorted so that rarer cards are more likely to appear near the bottom of the deck.
Here is a detailed article with pictures(!):
Try UCT search.
If it works, no AI is required. It's hard to characterize which games will work well, but a UCT framework is much easier to implement than traditional alpha-beta.
1) A Value Heuristic. You need a heuristic that the AI can evaluate how "good" a particular game state is. For example, in MTG, a very simple heuristic could assign 2 points for every card you have in play and 1 point for each point of life the enemy has, so one of the AI's goals would be, all other things being equal, to play as many cards as possible, but it would also be willing to sacrafice any card to do 2 or more damage to the enemy. Note that this heuristic probably depends on the deck in play -- for Mill decks, for example, you'd need to incorporate the opponent's deck size as a primary point source.
2) Move Lookahead. The game rules describe all the legal moves available to a player, including drawing/discarding/placing/activating a card. Each move can be rated according to the heuristic you came up with in step 1. However, this is only reading one move. More successful strategies should consider the opponent's subsequent move, and the AI's move after that, and so on. Typically you use a minimax strategy.
3) Further Optimization Naive move search increases exponentially the further ahead you search.. Using a technique similar to alpha/beta pruning, you don't evaluate paths that are really bad. If you can figure out a game state is strictly dominated by another game state, you do not have to continue looking down that branch of the move tree. Also, while the heuristic gives a general "sense of direction", the outcome of specific card combinations might not be apparent. Especially when using themed decks, you could keep a list of potent combos, especially gambits that take multiple steps before the heuristic could recognize the value of the combo.
Well, you have to do several things:
Thats the basics of it. Of course it may vary with how the game is itself.