Definition

An Expert System (ES) is a set of programs that manipulates some encoded knowledge to solve problems in a specialized domain that normally requires human expertise

OR

An ES is a computer application that solves complicated problems that would otherwise require human expertise.

Characterstic features of ES's

1. ES uses knowledge rather than data to control the solution process.Much of the knowledge used is heuristic in nature rather than algorithmic.
2. The knowledge is encoded and maintained as an entry separate from the control program and it is not compiled together with the control program.
3. ES's are capable of explaining how a particular conclusion was reached.This is important as it gives the user a chance to acesses and understand the systems reasoning ability,there by improving the users confidence in the system.
4. ES's use Symbolic representation for knowledge (rules,networks or frames) and perform their inference through symbolic computations that closely resemble manipulation of natural languages.
5. ES's often reason with Metaknowledge. i.e., they reason with knowledge about themselves,and their own knowledge limits and capabilities.

Block Diagram of a Typical ES

Components of a typical ES are:

• User Interface or I/O Interface: The user or I/O interface facility must accept information from the user and translate it into a form acceptable to the remainder of the system,or accept information from the system and convert it in to a form that can be understood by the user.
• Explanation Module: This provides the user with an explanation of the reasoning process when requested.The explanation consists of an identification of the steps in the reasoning process and a justification for each step.
• Knowledge Base(KB): It represents a storehouse of knowledge primitives.i.e., the knowledge base contains facts and rules about some specialized knowledge domain.
• Inference Engine(IE): It accepts user I/P's queues and responds to questions through the I/O interface and uses this dynamic information together with the static knowledge (rules and facts) stored in the KB.
• Learning Module or Knowledge Update: This is not a common component of ES.When they are provided,they are used to assist in building and refining the KB.The system in effect learns from experience and so is self updating or self learning.

Expert System Architectures

Rule Based System Architecture ( Production System )

This is the most common form of architecture used in expert and other knowledge based systems.This type of system uses knowledge encoded in the form of production rules. i.e., if..........then rules

IF : condition1 and condition2

THEN : take_action3

Each rule represents a small chunk of knowledge relating to the given domain of expertise.A number of related rules collectively may correspond to a chain of inferences which lead from some initially known facts to some useful conclusions.

Inference in production systems is achieved by a process of chaining through the rules recursively,either in a forward or backward direction until a conclusion is reached or until a failure occurs.The infering process is carried out recursively in 3 stages:

1. Match
2. Select
3. Execute

When the left side of a sequence of rules is instantiated first and the rules are executed from left to right,the process is called Forward Chaining or Data Driven Inference.

When the right side of the rules is instantiated first,the left hand side conditions become subgoals.These subgoals may in turn cause sub-subgoals to be established,and so on until facts are found to match the lowest subgoal condition.This is called Backward Chaining or Goal Driven Inference.

Non Production System Architectures

Instead of rules these systems employ more structured representation schemes like Sementic (Associative) Networks,Frames,Tree Structures (Decision tresss) or even Neural Networks.

Some of the Non Production System Architectures are

1. Sementic ( Associative ) Network Architectures

Sementic n/w representations are not a popular form of representation for standard ES's.More often these n/w representations are used in natural language or computer vision systems or in conjunction with some other form of representation.They are useful in depicting hierarchical knowledge structures where property inheritance is common.

Here the nodes represent objects,attributes,concepts or other basic entities and the arcs which are labelled describe the relationship between the nodes they connect.

Example: CASNET (Causal Associative Network) is used to diagnoise and recommend treatment for glaucoma,one of the leading causes of blindness.

2. Frame Architecture

Frames are structured sets of closely related knowledge such as on object or concept name,the objects main attribute and there corresponding values and possibly some attached procedures.The attributes,values and procedures are stored in specified slots and facets of the frame.

Individual frames are linked together in a network much like nodes in an associative n/w.Thus frames have many of the features of associative n/w's like property inheritance and default reasoning.

Example: PIP (Present Illness Program) was used to diagnoise patients using low costs,easily obtained information.

3. Decision Tree Architectures

Knowledge for ES may be stored in the form of a decision tree when the knowledge can be structured in a top to bottom manner.Initial & Intermediate nodes in the tree corresponds to the identities of the objects.Attribute values for an object determine a path to a leaf node in tree which contains the objects identification.

The KB which is the decision tree for an identification system can be constructed with a special tree building editor or with a learning module.New nodes or branches can be added to the tree when additional attributes are needed to further discriminate among new objects.As the system gains experience the values associated with the branches can be modified for more accurate results.

4. Black Board System Architecture

Blackboard architectures refer to a special type of KBS which uses a form of oppurtunistic reasoning.It uses both forward & backward chaining and chooses them dynamically at each stage in the problem solution process.Blackboard systems are composed of 3 functional components:

1.Knowledge Sources

2.Blackboard

3.Control Information

Example: HEARSAY a speech understanding system.

5. Analogical Reasoning Architectures

ES's based on analogical architectures solve new problems like humans,by finding a similar problem solution that is known and applying the known solution to the new problem,possibly with some modifications.

This would require a large KB having numerous problem solutions and other previously encountered situations or episodes.Each such situation should be stored as a unit in memory and be content indexed for rapid retreival.The inference mechanism must be able to extend known situations or solutions to fit the current problem and verify that the extendex solution is reasonable.

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