4.component of expert system

component of expert system in ai

1. Components of Expert System By Md. Fazle Rabbi 16CSE057

2. 4. 2 What is an Expert System ? Why Expert System ? Characteristics of Expert System Components of Expert System Advantages of Expert System Limitations of Expert System Outlines

3. 4. 3 An expert system is a computer program Designed to solve complex problems and to provide decision-making ability like a human expert. What is an Expert System?

4. 4. 4 What is an Expert System?

5. 4. 5 Examples of the Expert System: CaDeT : The CaDet expert system is a diagnostic support system that can detect cancer at early stages . DENDRAL: To detect unknown organic molecules with the help of their mass spectra and knowledge base of chemistry. PXDES: It is an expert system that is used to determine the type and level of lung cancer .

6. 4. 6 Why Expert System?

7. 4. 7 High Performance: The ES provides high performance for solving any type of complex problem. Understandable: It responds in a way that can be easily understandable by the user. Reliable: It is much reliable for generating an efficient and accurate output. Highly responsive: ES provides the result for any complex query within a very short period of time. Characteristics of Expert System

8. 4. 8 Components of Expert System

9. 4. 9 User Interface

10. 4. 10 The expert system interacts with the user. Takes queries as an input in a readable format, and passes it to the inference engine . It is an interface that helps a non-expert user to communicate with the expert system to find a solution . User Interface

11. 4. 11 Inference Engine(Rules of Engine)

12. 4. 12 Inference Engine(Rules of Engine) It is the brain of the expert system. It is the main processing unit of the system . It applies inference rules to the knowledge base to derive a conclusion. It helps in deriving an error-free solution of queries asked by the user .

13. 4. 13 Inference Engine(Rules of Engine) There are two types of inference engine : Deterministic Inference engine: The conclusions drawn from this type of inference engine are assumed to be true. It is based on facts and rules . Probabilistic Inference engine: This type of inference engine contains uncertainty in conclusions, and based on the probability.

14. 4. 14 Inference Engine(Rules of Engine) Inference engine uses the below modes to derive the solutions : Forward Chaining: It starts from the known facts and rules, and applies the inference rules to add their conclusion to the known facts . Backward Chaining: It is a backward reasoning method that starts from the goal and works backward to prove the known facts.

15. 4. 15 Knowledge Base

16. 4. 16 A knowledge base is an organized collection of facts about the system’s domain. Facts for a knowledge base must be acquired from human experts through interviews and observations. This knowledge is then usually represented in the form of “if-then” rules ( production rules ): “If some condition is true, then the following inference can be made (or some action taken ).” A probability factor is often attached to the conclusion of each production rule and to the ultimate recommendation, because the conclusion is not a certainty . Knowledge Base

17. 4. 17 Factual Knowledge It is the information widely accepted by the Knowledge Engineers and scholars in the task domain. Heuristic Knowledge It is about practice, accurate judgement, one’s ability of evaluation, and guessing . Components of Knowledge Base

18. 4. 18 The knowledge base is formed by readings from various experts, scholars,and the Knowledge Engineers . The knowledge engineer is a person with the qualities of empathy, quick learning, and case analyzing skills. He acquires information from subject expert by recording, interviewing, and observing him at work, etc. He then categorizes and organizes the information in a meaningful way, in the form of IF-THEN-ELSE rules , to be used by interference machine. The knowledge engineer also monitors the development of the ES. Knowledge Acquisition

19. 4. 19 Advantages of Expert System These systems are highly reproducible. They can be used for risky places where the human presence is not safe. Error possibilities are less if the KB contains correct knowledge. The performance of these systems remains steady as it is not affected by emotions, tension, or fatigue. They provide a very high speed to respond to a particular query.

20. 4. 20 Limitations of Expert System The response of the expert system may get wrong if the knowledge base contains the wrong information. Like a human being, it cannot produce a creative output for different scenarios. Its maintenance and development costs are very high. Knowledge acquisition for designing is much difficult. For each domain, we require a specific ES, which is one of the big limitations. It cannot learn from itself and hence requires manual updates.

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