Expert System for Knowledge-Based Reasoning - Revision Sheet

1. 📌 Essentials

• Expert systems simulate human decision-making using rules and facts.
• Core components: rules base, facts base, inference engine, user interface.
• Main inference methods: forward chaining, backward chaining, mixed chaining.
• Inference algorithms are akin to graph traversal techniques.
• Problem modeling and rule writing are major performance bottlenecks.
• Uncertainty is via probabilistic rules and facts.
• Applications include diagnosis, risk estimation, planning, and knowledge transfer.
• Accurate problem representation is critical for effective reasoning.
• System performance depends on efficient rule management and problem encoding.
• Inference flow is hierarchical and depends on the reasoning strategy.

2. 🧩 Key Structures & Components

• Rules Base — collection of IF-THEN rules defining knowledge.
• Facts Base — current known facts or data points.
• Inference Engine — processes rules and facts to derive conclusions.
• User Interface (UI) — facilitates interaction and data input.
• Inference Types:
  • Forward Chaining — data-driven, from facts to conclusions.
  • Backward Chaining — goal-driven, from goal to facts.
  • Mixed Chaining — combines both approaches.

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