Knowledge Representation in AI - Types, Issues, & Techniques
What is Knowledge Representation?
Knowledge Representation (KR) is the field of AI concerned with how information about the world is encoded so that a computer system can use it to solve complex tasks.
Types of Knowledge
- Declarative knowledge: Facts about the world.
- Procedural knowledge: How to perform tasks.
- Heuristic knowledge: Rules of thumb based on experience.
- Structural knowledge: Relationships between concepts.
Common Techniques
- Semantic Networks: Graph-based representation of concepts and relationships.
- Frames: Structured records representing stereotypical situations.
- Logic-based representation: Using propositional or predicate logic.
- Production Rules: If-then rules used in expert systems.
- Ontologies: Formal representation of knowledge as a set of concepts and relationships.
Issues in Knowledge Representation
- Handling incomplete or uncertain knowledge.
- Scalability as knowledge bases grow large.
- Balancing expressiveness with reasoning efficiency.
Key Takeaway: Mastering this topic is a key step toward becoming a well-rounded AI professional, capable of building real-world, intelligent systems.
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