CHAPTER 9: DECISION MAKING

Four most common categories of Artificial Intelligence (AI):

1. Genetic Algorithms - An artificial intelligent system that mimics the evolutionary, survival of the fittest process to generate increasingly better solutions to a problem. It essentially an optimizing system, it finds the combination of inputs that give the best outputs. Useful when each space very large or too complex for analytic treatment. In each iteration (generation) possible solutions or individuals represented as strings of numbers.

2. Intelligent Agents - Is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex, a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal.

3. Expert System - In the financial field is expert system for mortgages. Loan department are interested in expert systems for mortgages because of the growing cost of labor, which makes the handling and acceptance of relatively small loans less profitable. They also see a possibility for standardized, efficient handling of mortgages loan by applying expert systems, appreciating that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans.

4. Neutral Networks - Consider a real estate appraiser whose job is to predict the sale price of residential houses. As with the Bank Loans example, the input pattern consists of a group of numbers. For example, number of bedrooms, number of stories, floor area, age of construction, neighborhood prices, size of lot and distance to schools. This problem is similar to the Bank Loans example, because it has many non linearity and is subject to millions of possible inputs patterns. The different here is that the output prediction will consist of a calculated value the selling price of the house. It is possible to train the neural network to simulate the opinion of an expert appraiser or to predict the actual selling price.