Learning Haskell with Chess
From HaskellWiki
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===Learning targets=== | ===Learning targets=== | ||
*recapitulate Haskell types (keywords type and data, product and sum types) | *recapitulate Haskell types (keywords type and data, product and sum types) | ||
| - | *Helium: define equality functions (pattern matching) | + | **Helium: define equality functions (pattern matching) |
| + | **Haskell: define instances of type classes Show, Eq | ||
*pretty printing | *pretty printing | ||
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.
Contents |
1 Exercise 1 - data types
1.1 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
1.2 Tasks
- Define data types that represent boards (), squares (Board), positions (Square), pieces (Pos) and game states (Piece).State
- Helium: Implement suited eq-functions.
- Implement a function , that transforms a board into a clearly arranged string representation (human readable :-)). Support this function with auxiliary functions that pretty print pieces, squares, ...prettyBoard::Board->String
- Define the initial board (), test prettyBoard with initialBoard.initialBoard::Board
- Implement a simple evaluation function as the difference of material on board (values: Pawn->1, Knight and Bishop->3, Queen->9, Rook->6, King->"infinity"=1000).evalBoard::Board->Int
2 Exercise 2 - move generator
2.1 Learning targets
- list comprehension
- stepwise refinement
2.2 Tasks
3 Exercise 3 - gametree generation and minimax algorithm
3.1 Learning targets
- break code in modules
- complexity
- recursive data structures -> recursive algorithms
3.2 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 , that computes the value of a given game tree using the minimax Algorithm.play::Gametree->Int
- Implement the function , that choses the (best) next state.doMove::State->State
