# Difference between revisions of "Learning Haskell with Chess"

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− | This page is about learning Haskell using the board game Chess as a running example. The complete code can be found at | + | 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) | |

− | + | *pretty printing | |

− | |||

− | |||

− | + | ===Tasks=== | |

− | + | *Define data types that represent boards (<hask>Board</hask>), squares (<hask>Square</hask>), positions (<hask>Pos</hask>), pieces (<hask>Piece</hask>) and game states (<hask>State</hask>). | |

− | + | *Helium: Implement suited eq-functions. | |

− | + | *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. | |

− | + | *Implement a simple evaluation function <hask>evalBoard::Board->Int</hask> 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 (<hask>GameTree</hask>). | |

− | + | *Roughly estimate the number of nodes of the gametree with depth 4. | |

− | + | *Define a function <hask>play::Gametree->Int</hask>, that computes the value of a given game tree using the minimax Algorithm. | |

− | + | *Implement the function <hask>doMove::State->State</hask>, that choses the (best) next state. | |

− | |||

− | |||

− |

## Revision as of 16:34, 18 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: define equality functions (pattern matching)
- 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.