Advantages of Expert System.Doc

Topics: Expert system, Knowledge engineering, Decision theory Pages: 6 (1236 words) Published: November 6, 2012
Expert Systems

Advantages of Expert System:
1. Can be used by the user more frequently.
2. Can work round the clock.
3. Never "forgets" to ask a question, as a human might.
4. Encourages organizations to clarify the logic of their decision-making. 5. Holds and maintains significant levels of information. 6. Provides consistent answers for repetitive decisions, processes and tasks. Disadvantages of Expert System:

1. Lacks common sense needed in some decision making.
2. Cannot make creative responses as human expert would in unusual circumstances. 3. Domain experts not always able to explain their logic and reasoning. 4. Errors may occur in the knowledge base, and lead to wrong decisions. 5. Cannot adapt to changing environments, unless knowledge base is changed.

Advantages and disadvantages of rule-based expert systems

Rule-based expert systems are generally accepted as the best option for building knowledge-based systems.

Which features make rule-based expert systems particularly attractive for knowledge engineers? Among these features are:

Natural knowledge representation
. An expert usually explains the problem-solving procedure with such expressions as this: ‘in such-and-such situation, I do so-and so’. These expressions can be represented quite naturally as IF-THEN production rules.

Uniform structure
Production rules have the uniform IF-THEN structure. Each rule is an independent piece of knowledge. The very syntax of production rules enables them to be self-documented.

Separation of knowledge from its processing 
The structure of a rule-based expert system provides an effective separation of the knowledge base from the inference engine. This makes it possible to develop different applications using the same expert system shell. It also allows a graceful and easy expansion of the expert system. To make the system smarter, a knowledge engineer simply adds some rules to the knowledge base without intervening in the control structure. 50RULE-BASED EXPERT SYSTEMS


Dealing with incomplete and uncertain knowledge
Most rule-based expert systems are capable of representing and reasoning with incomplete and uncertain knowledge. For example, the rule IF season is autumn AND sky is ‘cloudy’ AND wind is low THEN forecast is clear { cf 0.1 };forecast is drizzle { cf 1.0 };forecast is rain { cf 0.9 }could be used to express the uncertainty of the following statement, ‘If the season is autumn and it looks like drizzle, then it will probably be another wet day today’. The rule represents the uncertainty by numbers called

Certainty factors

The expert system uses certainty factors to establish the degree of confidence or level of belief that the rule’s conclusion is true. This topic will be considered in detail in Chapter 3.All these features of the rule-based expert systems make them highly desirable for knowledge representation in real-world problems. Are rule-based expert systems problem-free?

There are three main shortcomings:
Opaque relations between rules
. Although the individual production rules tend to be relatively simple and self-documented, their logical interactions within the large set of rules may be opaque. Rule-based systems make it difficult to observe how individual rules serve the overall strategy. This problem is related to the lack of hierarchical knowledge representation in the rule-based expert systems. .

Ineffective search strategy
. The inference engine applies an exhaustive search through all the production rules during each cycle. Expert systems with a large set of rules (over 100 rules) can be slow, and thus large rule-based systems can be unsuitable for real-time applications. .

Inability to learn
. In general, rule-based expert systems do not have an ability to learn from the experience. Unlike a human expert, who knows when to ‘break the rules’, an expert system cannot automatically modify its knowledge base, or adjust existing rules or...
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