Abstract
Artificial intelligence is the stuff of science fiction writers, robots taking over the world, and computers knowing our every thought and action. Advanced methodologies is the utilization of accepted artificial intelligence programs in mathematical applications to solve a variety of problems. In this chapter, many of these methods will be described and sample applications provided to better explain the advantages of this method in problem solving.
TopBackground
Artificial intelligence has been around since the mid-twentieth century. Theory of its existence goes back much further, but it did not become a popular subject until the advent of digital computers. Its popularity varied over time and research in the field oscillated. As increasingly powerful computers became available, the subject had a renaissance. Many problems which were once called impossible or in the realm of AI, are now common and people take them for granted (Bostrum, 2006). Among these are such things as Optical Character Recognition (OCR), spell and grammar checkers, and speech recognition programs.
Key Terms in this Chapter
Genetic Algorithms: A computer program that is based upon the principles of genetic evolution and is effective at searching for solutions to complex problems. Also called evolutionary computing.
Forecasting: A process of predicting the future. Commonly applied to weather and equity prices.
Evolutionary Computing: Another name for genetic algorithms, an exploited search routine based upon biological evolution.
Neural Networks: A highly interconnected set of neurons that form a brain in animals and is often emulated in computer programs because of their power.
Credit Vetting: The process of examining data about a person or organization in order to determine their credit worthiness.
Pattern Recognition: The ability to see something and realize what it is and where it belongs. Humans are extremely adept at this and various forms of this are now computer processes.
Regression Analysis: A mathematical process of building a model based upon a set of data and identification of the variables.