Learning Haskell with Chess

From HaskellWiki

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 1.2 Tasks 2 Exercise 2 - move generator 2.1 Learning targets 2.2 Tasks 3 Exercise 3 - gametree generation and minimax algorithm 3.1 Learning targets 3.2 Tasks

1 Exercise 1 - data types

1.1 Learning targets

• recapitulate Haskell types (keywords
  type
  and
  data
  , product and sum types)
  • Helium: equality and show functions (pattern matching)
  • Haskell: type classes (
    Show
    ,
    Eq
    ,
    deriving
    )
• list handling (boards will be represented by lists of lists)

1.2 Tasks

• Define data types that represent boards (
  Board
  ), squares (
  Square
  ), positions (
  Pos
  ), pieces (
  Piece
  , supported by
  PieceColor
  and
  PieceType
  ) and game states (
  State
  ).
  • Helium: Implement suited eq and show functions.
  • Haskell: Define/derive instances of
    Show
    and
    Eq
• 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, for this purpose define a function
  valuePiece
  that maps pieces to their values (pawn->1, knight and bishop->3, queen->9, rook->5, king->"infinity"=1000).

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
  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.