How To Make An Efficient Ludo Game Playing AI Algorithm

I want to develop a ludo game which will be played by at most 4 playe开发者_开发知识库rs and at least two. One of the players will be an AI. As there is so many conditions I am not able to decide what pawn to move for the computer. I am trying my best but still to develop an efficient algorithm that can compete with human. If anybody knows the answers of any algorithm implemented in any language please let me know. Thanks.

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Also if you want you can try general game playing AI algorithm, such as monte carlo tree search. Basically idea is this - you need to simulate many random games from current move and after that choose such action which guarantees best outcome statistically.

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Basically, AI depends upon the type of environment. For the LUDO, environment is stochastic. There are multiple algorithms to decide what pawn should move next. for these types of environment, you need to learn algorithms like, "expectimax" , "MDP" or if you wanna make it more professionally you should go for "reinforcement learning".

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I think that in most computer card/board games, getting a reasonably good strategy for your AI player is better than trying to get an always-winning-top-notch algorithm. The AI player should be fun to play with.

Pretty reasonable way to do it is to collect a set of empirical rules which your AI should follow. Like 'If I got 6 on the dices I should move a pawn from Home before considering any other moves', 'If I have a chance to "eat" another player's pawn, do it', etc. Then range these rules from the most important to less important and implement them in the code. You can combine a set of rules into different strategies and try to switch them to see if AI plays better or worse.

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Start with a simple heuristic - what's the total number of squares each player has to move to get all their pieces home? Now you can make a few adjustments to that heuristic - for instance, what's the additional cost of a piece in the home square? (Hint - what's the expected total of the dice rolls before the player gets a six?). Now you can further adjust the 'expected distance' of pieces from home based on how likely they are to be hit. For instance, if a piece has a 1 in 6 chance of getting hit before the player's next move, then its heuristic distance is 5/6*(current distance)+1/6*(home distance).

You should then be able to choose a move that maximizes your player's advantage (difference in heuristic) over all the opponents.