Components of expert systems
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The document outlines the components of expert systems (ES), which include a knowledge base, inference engine, and user interface. The knowledge base holds factual and heuristic knowledge, while the inference engine processes this knowledge to reach solutions using various strategies. The user interface is designed for interaction, allowing users to understand the system's reasoning, though it emphasizes that expert systems have limitations in terms of technology, maintenance, and cost.
Components of expert systems
- 1. Swipe Components of Expert Systems AI
- 2. The components of ES include:- Knowledge Base Inference Engine User Interface Components of Expert Systems
- 3. Knowledge Base It contains domain-specific and high-quality knowledge. Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge. The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowledge.
- 4. Components of Knowledge Base The knowledge base of an ES is a store of both, factual and heuristic knowledge. 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. Knowledge representation It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.
- 5. Knowledge Acquisition The success of any expert system majorly depends on the quality, completeness, and accuracy of the information stored in the knowledge base. 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.
- 6. Inference Engine Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution. In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.
- 7. In case of rule based ES, it:- Applies rules repeatedly to the facts, which are obtained from earlier rule application. Adds new knowledge into the knowledge base if required. Resolves rules conflict when multiple rules are applicable to a particular case. To recommend a solution, the Inference Engine uses the following strategies :- Forward Chaining Backward Chaining
- 8. User Interface User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence. It explains how the ES has arrived at a particular recommendation.
- 9. The explanation may appear in the following forms :- Natural language displayed on screen. Verbal narrations in natural language. Listing of rule numbers displayed on the screen. The user interface makes it easy to trace the credibility of the deductions. Requirements of Efficient ES User Interface:- It should help users to accomplish their goals in shortest possible way. It should be designed to work for user’s existing or desired work practices. Its technology should be adaptable to user’s requirements; not the other way round. It should make efficient use of user input.
- 10. No technology can offer easy and complete solution. Large systems are costly, require significant development time, and computer resources. ESs have their limitations which include :- Limitations of the technology Difficult knowledge acquisition ES are difficult to maintain High development costs Expert Systems Limitations
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