3.2.14 Partitioned Semantic net | Chapter 3 | IT504 | Artificial Intelligence | RGPV

UNIT 3 | Artificial Intelligence

3.2.14 Partitioned Semantic net | Semantic Networks | IT504

Welcome to Unit 3 of our comprehensive Artificial Intelligence course!

In this video, we're delving deep into Unit 3, where we'll explore the intriguing world of Probabilistic Reasoning, Semantic Networks, Scripts, Schemas, Frames, Conceptual Dependency, Fuzzy Logic, Forward & Backward Reasoning, and much more. Get ready to unravel the mysteries of reasoning in AI!

Probabilistic Reasoning:
- Bayes’ Theorem: Discover the power of Bayes' Theorem, a fundamental concept in AI that enables us to make probabilistic inferences.
- Application of Bayes' Theorem in Artificial Intelligence: Explore real-world applications of Bayes' Theorem in AI.
- Bayesian Belief Network in Artificial Intelligence: Dive into Bayesian Belief Networks, a graphical model for representing probabilistic relationships.

Semantic Networks:
- Example 1 - 14: Understand the concept of semantic networks and explore various examples that demonstrate their use.
- Partitioned Semantic Net: Explore partitioned semantic networks and their applications through examples.

Scripts:
- Component: Learn about the components of scripts, a structured way to represent everyday activities.
- Example (Pseudo Form): Explore script examples in pseudo form.
- Advantages & Disadvantages: Understand the pros and cons of using scripts.
- Symbols of Actions: Discover the symbols used to represent actions in scripts.
- Examples using Actions: Dive into practical examples of scripts using action symbols.

Schemas:
- Learn about schemas, a fundamental concept in cognitive psychology and AI.

Frames:
- Introduction to Frames: Understand frames, a way to represent knowledge using structured units.
- Example 1 & 2 on Frames: Explore practical examples illustrating the use of frames.
- Advantages and Disadvantages of Frames: Evaluate the strengths and weaknesses of using frames in AI.
- Conceptual Dependency - Topic Introduction: Discover the concept of conceptual dependency and its role in AI.
- Rules: Explore the rules governing conceptual dependency.
- Examples: Dive into practical examples to grasp the application of conceptual dependency.
- Advantages and Disadvantages: Learn about the benefits and limitations of conceptual dependency.

Fuzzy Logic:
- Introduction: Understand the basics of fuzzy logic, a concept that deals with uncertainty and imprecision.
- Architecture: Explore the architecture of fuzzy logic systems.
- Classical Set Theory vs. Fuzzy Logic: Compare classical set theory with fuzzy logic to grasp their differences.
- Applications of Fuzzy Logic: Discover real-world applications where fuzzy logic shines.
- Advantages and Disadvantages: Evaluate the strengths and weaknesses of fuzzy logic in AI.
- Forward & Backward Reasoning
- Introduction: Understand the principles of forward and backward reasoning in AI.
- Example: Explore practical examples illustrating forward and backward reasoning.
- Difference between Forward and Backward Reasoning in AI: Learn how forward and backward reasoning differ and when to use each other.

Whether you're a beginner or an AI enthusiast, this unit will equip you with the knowledge and skills needed to understand and apply advanced reasoning techniques in artificial intelligence.

Don't forget to hit that subscribe button and ring the notification bell to stay updated with our upcoming videos. Let's journey through the realm of AI reasoning together!

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