Knowledge Representation is a field in AI focused on how to represent information about the world in a way that a computer system can use to solve complex problems—like diagnosing a disease, playing chess, or translating language.
Simply put:
It is the way AI "stores knowledge" so it can "think" or "reason" about it.
Reasoning is the process of deriving new knowledge from what is already known. AI systems use reasoning to draw conclusions, make decisions, or solve problems.
There are two main types of reasoning:
| Type | Description | Example |
|---|---|---|
| Deductive Reasoning | From general rules to specific facts | "All humans are mortal → Socrates is human → Socrates is mortal" |
| Inductive Reasoning | From specific facts to general rules | "The sun rose today, yesterday, and before → The sun always rises" |
Here are some major approaches:
| KR Method | Description | Used In |
|---|---|---|
| Logical Representation | Uses formal logic (e.g., Propositional Logic, First-Order Logic) | Theorem provers, expert systems |
| Semantic Networks | Graphs with nodes (concepts) and links (relations) | Natural language understanding |
| Frames | Data structures for stereotypical concepts (like objects in OOP) | Vision systems, robotics |
| Production Rules | If-Then rules | Expert systems (e.g., MYCIN) |
| Ontologies | Hierarchical structure of concepts | Semantic web, knowledge graphs |
Let’s say:
This is deductive reasoning using logical representation.
| Concept | Meaning |
|---|---|
| Knowledge Representation | How AI stores and organizes information |
| Reasoning | How AI uses knowledge to make decisions or draw conclusions |
| Importance | Core to making AI systems intelligent and useful |
| Techniques | Logic, rules, semantic networks, frames, ontologies |
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