An alternative approach for using software and hardware to solve problems is through the use of artificial intelligence systems. These systems attempt to mimic the workings of the human mind. Two types of artificial intelligence systems are covered in this section:
- Expert systems
- Neural networks
An expert system exhibits reasoning similar to that of a human expert to solve a problem. It accomplishes this reasoning by building a knowledge base of thedomain to be addressed in the form of rules and an inferencing mechanism to determine whether the rules have been satisfied by the system input.
Computer programs are usually defined as:
- algorithm + data structures = program
In an expert system, the relationship is:
- inference engine + knowledge base = expert system
The knowledge base contains facts and the rules concerning the domain of the problem in the form of if-then statements. The inference engine compares information it has acquired in memory to the if portion of the rules in the knowledge base to see whether there is a match. If there is a match, the rule is ready to “fire” and is placed in a list for execution. Certain rules may have a higher priority or salience, and the system will fire these rules before others that have a lower salience.
The expert system operates in either a forward-chaining or backward-chaining mode. In a forward-chaining mode, the expert system acquires information and comes to a conclusion based on that information. Forward chaining is the reasoning approach that can be used when there are a small number of solutions relative to the number of inputs. In a backward-chaining mode, the expert system backtracks to determine whether a given hypothesis is valid. Backward chaining is generally used when there are a large number of possible solutions relative to the number of inputs.
Another type of expert system is the blackboard. A blackboard is an expert system–reasoning methodology in which a solution is generated by the use of a virtual “blackboard,” on which information or potential solutions are placed by a plurality of individuals or expert knowledge sources. As more information is placed on the blackboard in an iterative process, a solution is generated.
As with human reasoning, there is a degree of uncertainty in the conclusions of the expert system. This uncertainty can be handled through a number of approaches, such as Bayesian networks, certainty factors, or fuzzy logic.
Bayesian networks are based on Bayes’ theorem:
P{H|E} = P{E|H}*P(H)/ P(E)
that gives the probability of an event H given that an event E has occurred.
Certainty factors are easy to develop and use. These factors are the probability that a belief is true. For example, a probability of 85 percent can be assigned to Object A occurring under certain conditions.
Fuzzy logic is used to address situations where there are degrees of uncertainty concerning whether something is true or false. This situation is often the case in real-world situations. A fuzzy expert system incorporates fuzzy functions to develop conclusions. The inference engine steps in fuzzy logic are as follows:
- Fuzzification. The membership functions defined on the input variables are applied to their actual values to determine the degree of truth for each rule premise.
- Inference. The truth-value for the premise of each rule is computed and applied to the conclusion part of each rule. This results in one fuzzy subset to be assigned to each output variable for each rule.
- Composition. All the fuzzy subsets assigned to each output variable are combined together to form a single fuzzy subset for each output variable.
- Defuzzification. Used when it is useful to convert the fuzzy output set to a quantitative number. One approach to defuzzification is the centroidmethod. With this method, a value of the output variable is computed by finding the variable value of the center of gravity of the membership function for the fuzzy output value.
The Spiral model can be used to build an expert system. The following are the common steps when building a Spiral model:
- Analysis
- Specification
- Development
- Deployment
A key element in this process is the acquisition of knowledge. This activity involves interviewing experts in the domain field and obtaining data from other expert sources. Knowledge acquisition begins in the specification phase and runs into the development phase.
Verification and validation of an expert system are concerned with inconsistencies inherent in conflicting rules, redundant rules, circular chains of rules, and unreferenced values along with incompleteness resulting from unreferenced or unallowable data values.
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