Difference between revisions of "Learning Haskell with Chess"
From HaskellWiki
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<h1>Exercise 2 - Move Generator</h1> | <h1>Exercise 2 - Move Generator</h1> | ||
<h2>Learning Targets</h2> | <h2>Learning Targets</h2> | ||
| + | <ul> | ||
| + | <li>list comprehension</li> | ||
| + | <li>stepwise refinement</li> | ||
| + | </ul> | ||
<h2>Tasks</h2> | <h2>Tasks</h2> | ||
Revision as of 14:15, 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 [1].
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.