Chapter: Artificial Intelligence(AI) - Expert System

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Expert System Features

The features which commonly exist in expert systems are :

Expert System Features

The   features which commonly exist in expert systems are :

Goal Driven Reasoning or Backward Chaining

An inference technique which uses IF-THEN rules to repetitively break a goal into smallersub-goals which are easier to prove;

Coping with Uncertainty

The ability of the system to reason with rules and data which are not precisely known;

Data Driven Reasoning or Forward Chaining

An inference technique which uses IF-THEN rules to deduce a problem solution from initial data;

Data Representation

 

The way in which the problem specific data in the system is stored and accessed;

User Interface

 

That portion of the code which creates an easy to use system;

 

Explanations

 

The ability of the system to explain the reasoning process that it used to reach a recommendation.

 

Each of these features were discussed in detail in previous lectures on AI. However for completion or easy to recall these are mentioned briefly here.

 

Goal-Driven Reasoning

Goal-driven reasoning, or backward chaining, is an efficient way to solve problems. The algorithm proceeds from the desired goal, adding new assertions found.

 


 

The knowledge is structured in rules which describe how each of the possibilities might be selected.

 

The rule breaks the problem into sub-problems. Example :

 

KB contains Rule set :

Rule 1:   If A and C     Then      F

Rule 2:   If A and E     Then      G

Rule 3:   If    B                  Then      E

Rule 4:   If    G                  Then      D

 

Problem : prove

 

If  A and B true   Then D is true

 

Uncertainty

 

 

Often the Knowledge is imperfect which causes uncertainty.

 

To work in the real world, Expert systems must be able to deal with uncertainty.

 

one simple way is  to associate a numeric value with each piece of information in the system.

 

the numeric value represents the certainty with which the information is known.

 

There are different ways in which these numbers can be defined, and how they are combined during the inference process.

 

Data Driven Reasoning

 

The data driven approach, or Forward chaining, uses rules similar to those used for backward chaining. However, the inference process is different. The system keeps track of the current state of problem solution and looks for rules which will move that state closer to a final solution. The Algorithm proceeds from a given situation to a desired goal, adding new assertions found.


The knowledge is structured in rules which describe how each of the possibilities might be selected. The rule breaks the problem into sub-problems.

 

Example :

 

KB contains Rule set :

 

Rule 1:    If A and   C     Then       F

Rule 2:    If A and   E     Then       G

Rule 3:    If B         Then       E

Rule 4:    If G        Then       D

Problem : prove                  

If     A and B true   Then D is true

 

Data Representation

 

Expert system is built around a knowledge base module.

knowledge      acquisition  is  transferring  knowledge  from  human  expert to computer.

 

Knowledge representation is faithful representation of what the expert knows.

 

No single knowledge representation system is optimal for all applications.

 

The success of expert system depends on choosing knowledge encoding scheme best for the kind of knowledge the system is based on.

 

The IF-THEN rules, Semantic networks, and Frames are the most commonly used representation schemes.

 

 

User Interface

The acceptability of an expert system depends largely on the quality of the user interface.

 

Scrolling dialog interface : It is easiest to implement and communicate with the user.

Pop-up menus, windows, mice are more advanced interfaces and powerful tools for communicating with the user; they require graphics support.

 

Explanations

An important features of expert systems is their ability to explain themselves.

Given that the system knows which rules were used during the inference process, the system can provide those rules to the user as means for explaining the results.

 

By looking at explanations, the knowledge engineer can see how the system is behaving, and how the rules and data are interacting.

 

This is very valuable diagnostic tool during development.


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