Chapter 9..desicion making

Four most common categories of all include:

Expert system...

A good example of application of expert systems in banking area is expert systems for mortgages. Loan departments 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 in the application of expert systems a possibility for standardized, efficient handling of mortgage loan, and appreciate that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans.
EXPERT SYSTEMS:
One of the largest area of applications of artificial intelligence is in expert systems, or knowledge based systems as they are often known. This type of system seeks to exploit the specialized skills or information held by of a group of people on specific areas. It can be thought of as a computerized consulting service. It can also be called an information guidance system. Such systems are used for prospecting medical diagnosis or as educational aids. They are also used in engineering and manufacture in the control of robots where they inter-relate with vision systems. The initial attempts to apply artificial intelligence to generalized problems made limited progress as we have seen but it was soon realized that more significant progress could be made if the field of interest was restricted.

 http://ict-patana.wikispaces.com/Examples+of+Expert+Systems 


Neutral network

 Attempts to emulate the way the human brain work.Artificial intelligence are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets.

http://www.cormactech.com/neunet/whatis.html 


example.....

Genetic algorithm

Genetic Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.
First pioneered by John Holland in the 60s, Genetic Algorithms has been widely studied, experimented and applied in many fields in engineering worlds. Not only does GAs provide an alternative methods to solving problem, it consistently outperforms other traditional methods in most of the problems link. Many of the real world problems involved finding optimal parameters, which might prove difficult for traditional methods but ideal for GAs. However, because of its outstanding performance in optimisation, GAs have been wrongly regarded as a function optimiser. In fact, there are many ways to view genetic algorithms. Perhaps most users come to GAs looking for a problem solver, but this is a restrictive view [ De Jong ,1993 ] .

example

Finance Applications

Models for tactical asset allocation and international equity strategies have been improved with the use of GAs. They report an 82% improvement in cumulative portfolio value over a passive benchmark model and a 48% improvement over a non-GA model designed to improve over the passive benchmark.
Genetic algorithms are particularly well-suited for financial modelling applications for three reasons:

1. They are payoff driven. Payoffs can be improvements in predictive power or returns over a benchmark. There is an excellent match between the tool and the problems addressed.
2. They are inherently quantitative, and well-suited to parameter optimization (unlike most symbolic machine learning techniques).
3. They are robust, allowing a wide variety of extensions and constraints that cannot be accommodated in traditional methods."

Intelligent agent

It is special purposed knowledge based information system that accomplishes specific tasks on behalf of its users.

Programs, used extensively on the web, that perform tasks such as retrieving and delivering information and automating repetitive tasks. More than 50 companies are currently developing intelligent agent software or services, including Firefly and WiseWire.
Agents are designed to make computing easier. Currently they are used as web browser, news retrieval mechanisms, and shopping assistants. By specifying certain parameters, agents will "search" the Internet and return the results directly back to your PC.
  Push technologyrelies on agents to deliver per-selected information to your desktop. Some intelligent agents are also used as tools to track Web behavior: they can even "watch" as your surf the Net and record how often you visit certain sites. Later, they can be used to automatically download your favorite sites, let you know when your favorite site has been updated, and even tailor specific pages to suit your tastes.


http://groups.engin.umd.umich.edu/CIS/course.des/cis479/projects/agent/Intelligent_agent.html