Bottleneck — key challenge?
Rule creation and problem representation
Inference types — mechanisms?
Forward, backward, mixed (graph traversal)
Expert system components?
Rules base, facts base, inference engine, UI
Expert systems — purpose?
Simulate human decision-making using rules and facts.
Core components of expert system?
Rules base, facts base, inference engine, user interface.
Main inference methods?
Forward chaining, backward chaining, mixed chaining.
Inference algorithms — similarity?
Graph traversal techniques.
Problem modeling — importance?
Key for effective reasoning, affects performance.
Uncertainty handling?
Probabilistic rules and facts.
Expert system applications?
Diagnosis, risk estimation, planning, knowledge transfer.
Answer 9 questions on Expert System Inference Techniques.
1. What is the primary function of system experts in knowledge-based systems?
2. Which inference method involves reasoning from goals to data in a knowledge-based system?
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