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I'm creating a logic game based on Fox and Hounds game. The player plays the fox and AI plays the hounds. (as far as I can see) I managed to make the AI perfect, so it never loses. Leaving it as such would not be much fun for human players.

Now, I have to dumb-down the AI so the human can win, but I'm not sure how. The current AI logic is based on pattern-matching - if I introduce random moves that make the board go out of pattern space the AI would most probably play dumb until the end of the game.

I'm also thinking about removing a set of patterns, so it would seem as AI does not know that "trick" but this way players could find a way to beat the computer using the same moves every time.

Any ideas on how to dumb down the AI in such a way that does not go from "genius" to "completely dumb" in a single move?

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There are several advances in the theory of loopy games and show that, with substantial computational assistance, many such games are just as tractable as their loop-free cousins.

Among these advances are several new techniques for simplifying loopy games. These techniques are implemented in the computer algebra system CGSuite. Three case studies are then analyzed that illustrate the power of the new methods: Fox and Geese, Backsliding Toads and Frogs, and Hare and Hounds. All three examples are well-known loopy games that had proved difficult to penetrate by hand.

You can create a couple of quasi-smart pattern plays (as if a 10-year-old might play) so it is not completely dumb, and then I pick one or two of those at random before the game starts. This way the game is always beatable, but the player does not know-how (i.e. he cannot use the same strategy to always win, he has to explore for the weak spots first).

For more https://epdf.pub/introduction-to-game-ai.htmlinformation refer to this link:

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