The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.

1. ARTIFICIAL INTELLIGENCE (AI) - KNOWLEDGE REPRESENTATION SCHEMESRuchi Sharmaruchisharma1701@gmail.comRuchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi

2. ContentsQuick Recall – AI concept

3. Knowledge Representation – Concept & Features

4. Knowledge Representation - Techniques/Schemes

5. Understanding Semantic Networks – Facts

6. Understanding Semantic Networks – Examples

7. Understanding Frames – Facts

8. Understanding Frames – Examples

9. Understanding Propositional Logic & FOPL – Facts

10. Understanding Propositional Logic & FOPL - Examples

11. Understanding Rule-based Systems - Facts

12. Understanding Rule-based Systems - ExamplesRuchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi

13. Quick Recall – AI ConceptsArtificial Intelligence deals with creating computer systems that can simulate human intelligent behaviour in a particular domain

14. learn new concepts and tasks

15. reason & draw conclusions

16. learn from the examples & past related experienceA computer possessing artificial intelligence( an expert system) has two basic parts Knowledge Base – containing the knowledge it uses

17. Inference-control unit – which facilitates the appropriate & contextual use of KBRuchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi