@hackage mines0.1

Mines 0.1

Building with Cabal:

$ runhaskell Setup.hs configure --user --prefix=$HOME $ runhaskell Setup.hs build $ runhaskell Setup.hs install

Use:

This part isn't too difficult. Run the program (called mines) from wherever you installed it and wait until you get some results. The output may give some hints on how well the sweepers are doing. Every 10 generations an SVG file is generated in ~/.mines (or Documents and Settings/user/Application Data/mines on Windows) that shows you how a minesweeper moved in the previous generation.

It may take a few hundred generations before they actually get smart. When they're smart enough for you, ctrl+c will exit.

Configuration:

To configure the program, you need to tweak the values in the beginning of Mines.hs and recompile. Changing settings like the size of the map or number of minesweepers makes the simulation run faster or slower and also affects the speed the minesweepers learn. The current values work pretty well, but feel free to experiment.

Neural networks:

The minesweepers have a 4-3-3-2 neural network, where the inputs are the direction of the minesweeper (x and y) and the vector to the nearest mine (x and y). The outputs are the angle where to turn (relative to current minesweeper direction) and the velocity for moving (which is not used in the code in this release).

To use the neural networks in NN.hs, you only need the functions newNeuralNet and updateNeuralNet. newNeuralNet takes a list of layers (such as [4,3,3,2]) as well as minimum and maximum values for weights (floats) as parameters, updateNeuralNet takes a weighting function (such as sigmoid or (*1)), list of weights as input and a neural net as a parameter and returns the list of outputs.

Genetic algorithms:

To train the neural networks you need genetic algorithms that can be found at GA.hs. You can use getWeights in NN.hs to get a list of weights that are the genes for the genetic algorithms, functions crossOver, mutate, mixGenes and pickRoulette for choosing the parents and creating the children and putWeights to apply the new weights to the new networks.

Todo:

The types for weights in neural networks as well as for genes in genetic algorithms are hard coded at the moment (Float). Making that application dependent would be good.

More info:

For questions, you can reach me at ajsalonen@gmail.com. I've written about the program in my blog, you can find the text at http://codeflow.wordpress.com/2008/08/03/about-ai-and-neural-networks/ Feel free to improve the code.

The minesweepers were inspired by an article by Mat Buckland on neural networks, available at http://www.ai-junkie.com/ann/evolved/nnt1.html

Licensed with MIT, see LICENSE for details. Copyright 2008 Antti Salonen.