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Questions and Answers
What is the primary function of the inference engine in an expert system?
Which of the following is a limitation of expert systems?
What is the main purpose of the knowledge base in an expert system?
Which of the following applications of expert systems involves assisting medical diagnosis?
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What is the main issue with knowledge representation in AI?
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What type of knowledge representation involves a set of pairs, 'if condition then action'?
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What is the main difference between semantic networks and frames?
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What is the main limitation of expert systems in terms of learning?
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Study Notes
Expert Systems
- An expert system is a machine that responds to a specific problem like an expert, reaching the same level of problem-solving ability as an expert.
- Three main components of an expert system: knowledge base, inference engine, and user interface.
Components of an Expert System
- Knowledge Base: stores information that the expert system relies on.
- Inference Engine: pulls relevant information from the knowledge base to solve the user's problem.
- User Interface: allows end users to interact with the system to get an answer to their question or problem.
Applications of Expert Systems
- Healthcare: used for medical diagnosis, e.g., CATDET (Cancer Decision Support Tool) to identify cancer in early stages.
- Customer Service: helps schedule and respond to customer requests and solve problems.
- Mechanical Engineering: explores complex mechanical machinery.
- Telecommunication: aids in making decisions about network technologies.
Weaknesses of Expert Systems
- Lack of Emotions: expert systems have no emotions.
- Common Sense: a major issue in expert systems.
- Domain Specificity: developed for a specific domain.
- Manual Updates: needs to be updated manually and does not learn itself.
- Lack of Transparency: cannot explain the logic behind its decisions.
Knowledge Representation
- Types of Knowledge: facts (believe & observe knowledge), procedures (how to knowledge), and meaning (relate & define knowledge).
- Importance of Representation: right representation is crucial, and a wrong choice can lead to project failure.
Knowledge Representation Methods
- Logical Representations
- Production Rules: a set of rules with "if condition then action" pairs.
- Semantic Networks: conceptual graphs, where each graph represents a single proposition.
- Frames: semantic networks where nodes have structure.
- Description Logics
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Description
Learn about the basics of expert systems, including their components and how they work. Understand the role of knowledge base, inference engine, and user interface.
