Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Evolutionary Artificial Neural Network Weight Tuning to Optimize Decision Making for an Abstract Game

Evolutionary Artificial Neural Network Weight Tuning to Optimize Decision Making for an Abstract Game

Miller, Corey M.

Paperback

General ComputersGeneral EducationGeneral Science

ISBN10: 1288307098
ISBN13: 9781288307098
Publisher: Biblioscholar
Published: Nov 16 2012
Pages: 100
Weight: 0.33
Height: 0.21 Width: 6.14 Depth: 9.21
Language: English

Abstract strategy games present a deterministic perfect information environment with which to test the strategic capabilities of artificial intelligence systems. With no unknowns or random elements, only the competitors' performances impact the results. This thesis takes one such game, Lines of Action, and attempts to develop a competitive heuristic. Due to the complexity of Lines of Action, artificial neural networks are utilized to model the relative values of board states. An application, pLoGANN (Parallel Lines of Action with Genetic Algorithm and Neural Networks), is developed to train the weights of this neural network by implementing a genetic algorithm over a distributed environment. While pLoGANN proved to be designed efficiently, it failed to produce a competitive Lines of Action player, shedding light on the difficulty of developing a neural network to model such a large and complex solution space.

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