Accession Number : ADA516710


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


Descriptive Note : Master's thesis


Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT


Personal Author(s) : Miller, Corey M


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a516710.pdf


Report Date : Mar 2010


Pagination or Media Count : 98


Abstract : 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.


Descriptors :   *DECISION MAKING , *GAME THEORY , *NEURAL NETS , *ARTIFICIAL INTELLIGENCE , GENETIC ALGORITHMS , HEURISTIC METHODS , DETERMINATION , EVOLUTION(DEVELOPMENT) , WEIGHT , THESES , OPTIMIZATION , MILITARY STRATEGY , TUNING


Subject Categories : Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE