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Elements Of Rule Based Expert System

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RULE BASED EXPERT SYSTEMS

November 2015
Table of Contents

Abstract 1
1. Elements of a Rule Based Expert System: 2
 Rules 2
 USER INTERFACE 2
 EXPLANATION FACILITY 3
 WORKING MEMORY 3
 INFERENCE ENGINE 4
 AGENDA 4
 KNOWLEDGE ACQUISITION FACILITY 4
2. Architecture of a Rule Based System: 5
3. Theory of Rule Based Systems: 6
4. Advantages of Rule Based Expert Systems: 7
5. Conclusion/Summing Up/In Summary 7
6. How will your use case bring value to IGATE business/vertical 7
7. References 8
8. About the Authors 8

Abstract

Rule based systems are the simplest form of artificial intelligence.These were invented in the early 1970’s and are still in use today.
Rules are the popular paradigm for representing knowledge. They …show more content…

Elements of a Rule Based Expert System:
A rule based expert system consists of the following components:
 Rules
Any rules consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action)
IF
THEN
A rule can have multiple antecedents joined by the keywords AND (conjunction), OR (disjunction) or a combination of both.
IF AND/OR
THEN
Rules can represent:
· Relation: IF the ‘fuel tank’ is empty THEN the car is dead
· Recommendation: IF the season is autumn AND the sky is cloudy AND the forecast is drizzle THEN the advice is ‘take an umbrella’
· Directive: IF the car is dead AND the ‘fuel tank’ is empty THEN the action is ‘refuel the car’
· Strategy: IF the car is dead THEN the action is ‘check the fuel tank’; step1 complete IF step1 is complete AND the ‘fuel tank’ is full THEN the action is ‘check the battery’; step2 is complete
· Heuristic: IF the spill is liquid AND the ‘spill pH’ < 6 AND the ‘spill smell’ is vinegar THEN the ‘spill material’ is ‘acetic acid’
 USER INTERFACE
This is a mechanism to support communication between the user and the system. The user interface may be a simple text-oriented display or a sophisticated, high resolution graphic display. It is determined at the time of designing the system. Nowadays graphical user interfaces are very common for their …show more content…

It decides which rules are satisfied by the facts, prioritizes them, and executes the rule with the highest priority. There are two types of inference: forward chaining and backward chaining. Forward chaining is reasoning from facts to the conclusion while backward chaining is from hypothesis to the facts that support this hypothesis. Whether an inference engine performs forward chining or backward chaining entirely depends on the design which in turn depends on the type of problem. Some of the systems that do forward chaining are OPS5 and CLIPS. E-MYCIN one of the most popular systems performs backward chining. Some systems, ART and KEE, for example, offer both the techniques. Forward chaining is best suited for prognosis, monitoring and control. Backward chaining is generally used for diagnostic problems. E-MYCIN deduces the list of possible culprit bacteria based on symptoms provided by the physician. Inference engine operates in cycles, executing a group of tasks until certain criteria causes that halt the execution. The tasks to be done repeatedly are conflict resolution, act, match and check for halt. Multiple rules may be activated and put on the agenda during one

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