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- W2530969645 abstract "This dissertation provides a framework for incorporating intelligent objects using Fuzzy ARTMAP neural networks, into existing simulations to more realistically model human decision making. This approach significantly reduces the need for updating and maintaining the behavioral aspects of the code. The framework enables the intelligent computer generated objects (ICGO) to learn from existing computer generated object rules, as well as learn from human operators participating in the simulation. Additional features such as pruning, and rule extraction, are incorporated into the methodology to increase its utility and provide a straightforward mechanism for describing rules that govern decision episodes. The final product is an ICGO that exhibits certain human behavioral traits including an ability to adapt to changes and basing decisions on previous experiences. The algorithmic aspects of the methodology are validated using benchmark databases and three test case scenarios are used to demonstrate the various tasks that comprise the methodology. These scenarios are also used to demonstrate the ICGO's ability to alter its decision making process in real-time through a simple user interface." @default.
- W2530969645 created "2016-10-21" @default.
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- W2530969645 date "2001-01-01" @default.
- W2530969645 modified "2023-09-23" @default.
- W2530969645 title "A methodology for modeling human decision making in computer generated objects" @default.
- W2530969645 hasPublicationYear "2001" @default.
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