Difference between revisions of "Learning Haskell with Chess"
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===Learning targets=== |
===Learning targets=== |
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*recapitulate Haskell types (keywords type and data, product and sum types) |
*recapitulate Haskell types (keywords type and data, product and sum types) |
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− | *Helium: define equality functions (pattern matching) |
+ | **Helium: define equality functions (pattern matching) |
+ | **Haskell: define instances of type classes Show, Eq |
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*pretty printing |
*pretty printing |
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Revision as of 08:28, 19 March 2007
This page is about learning Haskell using the board game Chess as a running example. The complete code can be found at http://www.steffen-mazanek.de/dateien/projekte/hsChess.zip.
Exercise 1 - data types
Learning targets
- recapitulate Haskell types (keywords type and data, product and sum types)
- Helium: define equality functions (pattern matching)
- Haskell: define instances of type classes Show, Eq
- pretty printing
Tasks
- Define data types that represent boards (
Board
), squares (Square
), positions (Pos
), pieces (Piece
) and game states (State
). - Helium: Implement suited eq-functions.
- Implement a function
prettyBoard::Board->String
, that transforms a board into a clearly arranged string representation (human readable :-)). Support this function with auxiliary functions that pretty print pieces, squares, ... - Define the initial board (
initialBoard::Board
), test prettyBoard with initialBoard. - Implement a simple evaluation function
evalBoard::Board->Int
as the difference of material on board (values: Pawn->1, Knight and Bishop->3, Queen->9, Rook->6, King->"infinity"=1000).
Exercise 2 - move generator
Learning targets
- list comprehension
- stepwise refinement
Tasks
Exercise 3 - gametree generation and minimax algorithm
Learning targets
- break code in modules
- complexity
- recursive data structures -> recursive algorithms
Tasks
- Define a data type that represents a game tree (
GameTree
). - Roughly estimate the number of nodes of the gametree with depth 4.
- Define a function
play::Gametree->Int
, that computes the value of a given game tree using the minimax Algorithm. - Implement the function
doMove::State->State
, that choses the (best) next state.