# Difference between revisions of "Learning Haskell with Chess"

From HaskellWiki

Line 4: | Line 4: | ||

===Learning targets=== |
===Learning targets=== |
||

− | *recapitulate Haskell types (keywords type and data, product and sum types) |
+ | *recapitulate Haskell types (keywords <hask>type</hask> and <hask>data</hask>, product and sum types) |

**Helium: equality and show functions (pattern matching) |
**Helium: equality and show functions (pattern matching) |
||

− | **Haskell: type classes (<hask>Show</hask>, <hask>Eq</hask>, <hask>deriving</hask> |
+ | **Haskell: type classes (<hask>Show</hask>, <hask>Eq</hask>, <hask>deriving</hask>) |

− | * |
+ | *list handling (boards will be represented by lists of lists) |

===Tasks=== |
===Tasks=== |
||

Line 14: | Line 14: | ||

**Haskell: Define/derive instances of <hask>Show</hask> and <hask>Eq</hask> |
**Haskell: Define/derive instances of <hask>Show</hask> and <hask>Eq</hask> |
||

*Implement a function <hask>prettyBoard::Board->String</hask>, that transforms a board into a clearly arranged string representation (human readable :-)). Support this function with auxiliary functions that pretty print pieces, squares, ... |
*Implement a function <hask>prettyBoard::Board->String</hask>, 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 (<hask>initialBoard::Board</hask>), test prettyBoard with initialBoard. |
+ | *Define the initial board (<hask>initialBoard::Board</hask>), test <hask>prettyBoard</hask> with <hask>initialBoard</hask>. |

− | *Implement a simple evaluation function <hask>evalBoard::Board->Int</hask> as the difference of material on board |
+ | *Implement a simple evaluation function <hask>evalBoard::Board->Int</hask> as the difference of material on board, for this purpose define a function <hask>valuePiece</hask> that maps pieces to their values (pawn->1, knight and bishop->3, queen->9, rook->5, king->"infinity"=1000). |

==Exercise 2 - move generator== |
==Exercise 2 - move generator== |

## Revision as of 08:39, 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

## Exercise 1 - data types

### 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)

### 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).

## 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.