Yes. It is feasible.
Overview of the Question
The design goal of the system seems to be gain a winning strategic advantage by employing one or more artificial networks in conjunction with a card game playing engine.
The question shows a general awareness of the basics of game-play as outlined in Morgenstern and von Neuman's Game Theory.
- At specific points during game-play a player may be required to execute a move.
- There is a fininte set of move options according to the rules of the game.
- Some strategies for selecting a move produce higher winning records over multiple game plays than other strategies.
- An artificial network can be employed to produce game-play strategies that are victorious more frequently that random move selection.
Other features of game-play may or may not be as obvious.
- At each move point there is a game state, which is needed by any component involved in improving game-play success.
- In addition to not knowing when the opponent will bluff, in card games, the secret order of shuffled cards can introduce the equivalent of a virtual player the moves of which approximate randomness.
- In three or more player games, the signaling of partners or potential partners can add an element of complexity to determining the winning game strategy at any point. Based on the edits, it does not appear like this game has such complexities.
- Psychological factors such as intimidation can also play a role in winning game-play. Whether or not the engine presents a face to the opponent is unknown, so this answer will skip over that.
Common Approach Hints
There is a common approach to mapping both inputs and outputs, but there is too much to explain in a Stack Exchange answer. These are just a few basic principles.
- All of the modeling that can be done explicitly should be done. For instance, although an artificial net can theoretically learn how to count cards (keeping track of the possible locations of each of the cards), a simple counting algorithm can do that, so use the known algorithm and feed those results into the artificial network as input.
- Use as input any information that is correlated with optimal output, but don't use as inputs any information that can not possibly correlate with optimal output.
- Encode data to reduce redundancy in the input vector, both during training and during automated game-play. Abstraction and generalization are the two common ways of achieving this. Feature extraction can be used as tools to either abstract or generalize. This can be done at both inputs and outputs. An example is that if, in this game, J > 10 in the same way that A > K, K > Q, Q > J and 10 > 9, then encode the cards as an integer from 2 through 14 or 0 through 12 by subtracting one. Encode the suits as 0 through 3 instead of four text strings.
The image recognition work is only remotely related, too different from card game-play to use directly, unless you need to recognize the cards from a visual image, in which case LSTM may be needed to see what the other players have chosen for moves. Learning winning strategies would more than likely benefit from MLP or RNN designs, or one of their derivative artificial network designs.
What an Artificial Network Would Do and Training Examples
The primary role of artificial networks of these types is to learn a function from example data. If you have the move sequences of real games, that is a great asset to have for your project. A very large number of them will be very helpful for training.
How you arrange the examples and whether and how you label them is worth consideration, however without the card game rules it is difficult to give any reliable direction. Whether there are partners, whether it is score based, whether the number of moves to a victory, and a dozen other factors provide the parameters of the scenario needed to make those decisions.
The main advise I can give is to read, not so much general articles on the web, but read some books and some of the papers you can understand on the above topics. Then find some code you can download and try after you understand the terminology well enough to know what to download.
This means book searches and academic searches are much more likely to steer you in the right direction than general web searches. There are thousands of posers in the general web space, explaining AI principles with a large number of errors. Book and academic article publishers are more demanding of due diligence in their authors.