09-07-2013, 04:16 PM
Intelligent Decision-Making Support Systems (iDMSS)
Intelligent Decision.ppt (Size: 203 KB / Downloads: 45)
Introduction
An i-DMSS extends traditional DSS by incorporating techniques to supply intelligent behaviors and utilizing the power of modern computers to support and enhance decision making.
Intelligent System
Intelligent systems should be able to:
(i) learn or understand from experience;
(ii) make sense out of ambiguous or contradictory messages;
(iii) respond quickly and successfully to a new situation;
(iv) use reasoning in solving problems and directing conduct effectively;
(v) deal with perplexing situations;
(vi) understand and infer in ordinary, rational ways;
(vii) apply knowledge to manipulate the environment;
(viii) think and reason; and
(ix) recognize the relative importance of different elements in a situation.
Neural Computing
Neural Computing is a problem solving methodology that attempts to mimic how our brains function.
Knowledge representations based on
Massive parallel processing
Fast retrieval of large amounts of information
The ability to recognize patterns based on historical cases
Neural Computing = Artificial Neural Networks (ANNs)
Artificial Neural Networks
ANN can help to automate complex decision making
Neural networks learn from past experience and improve their performance levels
Machine learning: Methods that teach machines to solve problems, or to support problem solving, by applying historical cases
Example: Loan Approval decision Making
Loan approval decision making use many variables: Customers income, employment history, credit history, outstanding debts, and so on. Capturing them in a software is difficult.
Fast decision making on loans is beneficial: make decision while customer is still in the office!
A neural network was trained to recognize patterns of successful and unsuccessful loans based on past history. The NN is fed with risk, the interest rate, customer data, and other variables.
A NN can quickly recommend approval or denial of a loan. It can also detect Fraud.
Application and properties of Neural Networks
Pattern recognition, learning, classification, generalization and abstraction, and interpretation of incomplete and noisy inputs
Character, speech and visual recognition
Can provide some human problem solving characteristics
Can tackle new kinds of problems
Robust
Fast
Flexible and easy to maintain
Powerful hybrid systems