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An Introduction to Expert Systems

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An Insight into Expert Systems

Ð'¬Ð'¬Ð'¬Rahul Srinivasan Aurora

3rd Year, E.C.E.

Vasavi College of Engineering



To improve speed of operations, programming practices for practical purposes, are moving away from the data centric, procedural problem solving paradigm to a heuristic, declarative problem solving paradigm. Though theoretically there is no guarantee that a solution shall be found and even if it is found, that it be correct, practically it has been proven that expert systems employing , heuristics are indeed a faster and more effective manner of problem solving , with an added advantage of having an explanation for the answer arrived at. Having started out as a diagnostic tool, it has now found acceptance all over, be it Manufacturing Firms or IT Solution Providers and is definitely here to stay. Its dependence on Artificial Intelligence furthermore proves its capabilities to branch out to more areas of deployment. With the advent of commercial-off-the-shelf expert system development tools making the process of designing an expert system a simple task, now the real challenge lies with the experts to be able to put these their knowledge and expertise in their domain to effective use to create systems which can be put to use effectively.

Expert Systems are a branch of Artificial Intelligence that makes an extensive use of specialized knowledge to solve problems at the level of a human expert. AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior.

The term intelligence covers many cognitive skills, including the ability to solve problems, learn, and understand language; AI addresses all of those. But most progress to date in AI has been made in the area of problem solving -- concepts and methods for building programs that reason about problems rather than calculate a solution.

Procedural programs are preferred when there is much data to be manipulated and very less IF..THEN statements, i.e. not much of decision making to be implemented. But once the number of decisions to be made increases based on the problem, the solution tends to become a lot more complex. This is where expert systems take over, in employing methods to reason about the problem at hand.

The need for expert systems can be attributed to the following:

• Increased availability

• Permanence

• Reduced Danger

• Reduced Cost

• Multiple expertise

• Increased Reliability

• Explanation facility

• Fast Response

• Steady, emotional & complete response

• Intelligent tutor

Expert System Components:

• Working Memory

вЂ" A global database of facts used by the system

• Knowledge Base

вЂ" Contains the domain knowledge

• Inference Engine

вЂ" The brain of the Expert system. Makes logical deductions based upon the knowledge in the Knowledge Base.

• User Interface

вЂ" A facility for the user to interact with the Expert system.

• Explanation Facility

вЂ" Explains reasoning of the system to the user

• Knowledge Acquisition Facility

вЂ" An automatic way to acquire knowledge

Knowledge Base

The knowledge base ( KBS)of expert systems contains both factual and heuristic knowledge. Factual knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field.

Heuristic knowledge is the less rigorous, more experiential, more judgmental knowledge of performance. It is the knowledge of good practice, good judgment, and plausible reasoning in the field. It is the knowledge that underlies the "art of good guessing."

Knowledge representation formalizes and organizes the knowledge. The two most widely used representations are

вЂ" Production Rules: A rule consists of an IF part and a THEN part (also called a condition and an action). if the IF part of the rule is satisfied; consequently, the THEN part can be concluded, or its problem-solving action taken. Rule based expert Systems use this representation.

вЂ" Frames or units: The unit is an assemblage of associated symbolic knowledge about the represented entity. Typically, a unit consists of a list of properties of the entity and associated values for those properties.

The inference engine involves the decisions of which, and in what order, the rules to be selected for evaluation of the knowledge base. It is a program that interprets the rules in the knowledge base in order to draw conclusions. An inference engine has three main elements. They are:

1. An interpreter. The interpreter executes the chosen agenda items by applying the corresponding base rules.

2. A scheduler. The scheduler maintains control over the agenda by estimating the effects of applying inference rules in light of



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