So i was reading "Programming Game AI by Example" by Mat Buckland (http://www.ai-junkie.com/books/toc_pgaibe.html) and decided to rewrite his chapter on Fuzzy logic in haskell (from C++). My initial impression: its one of those scenarios where OOP grossly over complicates things Heres an example taken from the book: An agent needs to decide what weapon to use based on his distance from the target and the amount of ammo each has. The result is in the desirability domain (0-100). http://code.haskell.org/~hexpuem/fuzzyLogic/AI/Logic/Fuzzy/Example.hs An excerpt: ------------------------------------ weapons = [ Weapon "RocketLauncher" 9 rocketLauncher, Weapon "ShotGun" 13 shotgun, Weapon "AssaultRifle" 120 assaultRifle, Weapon "SniperRifle" 7 sniperRifle, Weapon "Knife" 1 knife ] chooseWeapon dist = maximum ratings where ratings = [ (f dist ammo, n) | Weapon n ammo f <- weapons ] ------------------------------------ shotgun :: Double -> Double -> Double shotgun dist ammo = unfuzz desireDom $ rules (fuzz distDom dist) (fuzz ammoDom ammo) where rules :: Fuzzy Distances -> Fuzzy Ammo -> FL Desirability rules distance ammo = do distance `is` SniperSuited =>> Pointless distance `is` Far =>> Undesirable distance `is` Medium =>> Undesirable distance `is` Melee =>> Undesirable (distance `fairly` Near) & (ammo `fairly` High) =>> VeryDesirable (distance `fairly` Near) & (ammo `fairly` Good) =>> Desirable (distance `fairly` Near) & (ammo `is` Low) =>> Undesirable ------------------------------------ Full code at http://code.haskell.org/~hexpuem/fuzzyLogic/. Suggestions welcome - maybe it'd be useful to upload to hackage at some point. It only supports triangle, shoulder, and singleton memberships at the moment.