Survey several basic AI techniques implemented with short, open-source Python code recipes. Appropriate for educators and programmers who want to experiment with AI and apply the recipes to their own problem domains. For each technique, learn the basic operating principle, discuss an approach using Python, and review a worked out-example. We'll cover database mining using neural nets, automated categorization with a naive Bayesian classifier, solving popular puzzles with depth-first and breath-first searches, solving more complex puzzles with constraint propagation, and playing a popular game using a probing search strategy. Slides: http://us.pycon.org/media/2009/talkdata/PyCon2009/071/PyCon2009_AI_Alt.ppt 00:00 Intro 05:00 Eight Queens - Six Lines http://code.activestate.com/recipes/576647/06:45 Alphametics Solver http://code.activestate.com/recipes/576615/ 11:30 Neural Nets for Data Mining http://code.activestate.com/recipes/496908-data-mining-with-neural-nets/ 16:20 Each unique value gets a neuron 22:10 Code 22:30 Mastermind http://code.activestate.com/recipes/496907/31:20 Shannon's formula applied to strategy - select probe with greatest information 33:55 Sudoku-style Puzzle http://code.activestate.com/recipes/473893-sudoku-solver/ 38:50 Bayesian Classifier http://www.divmod.org/projects/reverend or http://sourceforge.net/projects/reverend/43:30 Code 44:50 Hettinger http://users.ren.com/python/download/puzzle.py47:30 Q&A
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