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EXPERT SYSTEMS

Expert Systems. An expert system behaves similar to a human expert in a field or areaThey can be used to solve problems in various fields or disciplines, and can assist in problem-solvingGoal is not to replace experts but to provide users with an effective tool thereby relieving experts of routine

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EXPERT SYSTEMS

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    1. EXPERT SYSTEMS

    2. Expert Systems An expert system behaves similar to a human expert in a field or area They can be used to solve problems in various fields or disciplines, and can assist in problem-solving Goal is not to replace experts but to provide users with an effective tool thereby relieving experts of routine tasks

    4. Expert System Capabilities Superior problem solving- only solvable problems Ability to save and apply knowledge and experience to problems Reduced time for complex problems Looks at problems from a variety of perspectives

    5. Uses of an Expert System Strategic goal setting Planning Design Scheduling Monitoring Diagnosis

    6. Where do you need an Expert System? If you need to capture and preserve irreplaceable human experience achieve less errors distribute expertise to multiple locations at the same time

    7. Some Disadvantages Of an Expert System Can never replace a human expert completely Expertise can be tough to gather and tougher to code Cost- might take some time to develop Limited to solvable problems Experts are only human and can make mistakes

    8. Components 5 Basic Components Knowledge Base Inference Engine Explanation Component User Interface Acquisition Component

    10. Knowledge Base contains the factual and empirical knowledge of experts in a particular subject area contains all the facts, rules and procedures which are important for problem solving If (premise) Then conclusion and/or action

    11. Inference Engine simulates the problem-solving strategy of a human expert Represents the logical unit by means of which conclusions are drawn from the knowledge base according to a defined problem-solving method Controls the execution- which questions to ask, and in what order simulates the problem-solving process of human experts Example- A rule states: If p and q, then r The facts are: p and q from the facts p and q we can conclude fact r

    12. Functions of the Inference Engine determines which actions are to be executed between individual parts of the expert system, how they are executed and in what sequence determines how and when the rules will be processed controls the dialog with the user

    13. Explanation Component explains the problem-solving strategy to the user the solutions must be reproducible by the user and engineer but can only be verified by the human expert Which facts were asked for? Why? Which facts were vital? Can go as far as- How would the conclusion change if some facts would change?

    14. User Interface employs natural language for dialogs with the user whenever possible Questions posed such as How should questions be answered by the user? How will system responses to these questions will be formulated? What info is to be graphical? Must be easy to use, erroneous errors kept to a minimum, questions and answers must be understandable

    15. Acquisition Component provides support for the structuring and implementation of the knowledge in the knowledge base Very Important allows engineer to concentrate less on programming Knowledge should be easy to enter Easy to understand methods of representing all info in knowledge base syntax checks access to programming language

    16. People involved with Expert Systems Domain Expert a person who possesses the skill and knowledge to solve a specific problem in a manner superior to others Knowledge Engineer A person who designs, builds and tests an expert system End-User The individual or group who will be using the experts system. It key that the expert system meets their needs!!!

    17. Inference Techniques The two main techniques used are: Forward-chaining Backward-chaining

    18. Forward Chaining In this process the knowledge base is searched for rules that match the known facts, and the action part of these rules is performed.The process continues until a goal is reached. Puts the symptoms together to reach a conclusion ex. Doctor diagnosing a patient

    19. Backward Chaining Starts form a goal, the conclusion. All the rules that contain this conclusion are then checked to determine whether the conditions of these rules have been satisfied Ex. Doctor has end idea of what is wrong with patient but know they must prove it by going from the diagnosis and finding symptoms

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