Knowledge Representation and Reasoning in Artificial Intelligence (AI)

Knowledge Representation and Reasoning in Artificial Intelligence

Knowledge representation and reasoning are core areas of Artificial Intelligence (AI) that focus on how machines can store, retrieve, and reason with information effectively.

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📚 Knowledge Representation Issues

Knowledge representation involves encoding information about the world into a form that a computer system can understand and use.

Key Issues in Knowledge Representation:

• Representation Accuracy: The ability to represent knowledge precisely.
• Inferential Adequacy: The ability to infer new knowledge from existing information.
• Inferential Efficiency: The speed and efficiency of reasoning mechanisms.
• Acquisition and Learning: How easily knowledge can be acquired, modified, and extended.

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🔢 Predicate Logic

Predicate Logic is a formal language used to represent facts and rules about objects and their relationships.

✅ Logic Programming:

• Based on formal logic.
• Uses facts, rules, and queries for reasoning.
• Commonly implemented using languages like Prolog.

Example:

father(john, mary).
father(john, david).
parent(X, Y) :- father(X, Y).

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🌐 Semantic Networks, Frames & Inheritance

✅ Semantic Networks:

• Graph-based knowledge representation.
• Uses nodes for concepts and edges for relationships.

✅ Frames:

• Structured representations for stereotyped situations.
• Each frame holds attributes (slots) and their associated values.

✅ Inheritance:

• Allows sharing properties between frames.
• Child frames inherit attributes from parent frames.

Example (Frame with Inheritance):

Frame: Animal
  Legs: 4
  Can Move: Yes

Frame: Dog (inherits Animal)
  Sound: Bark

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🔗 Constraint Propagation

Constraint propagation is a technique used in reasoning systems to reduce the search space by applying constraints to eliminate invalid solutions early.

Applications:

• Solving puzzles (like Sudoku)
• Optimization problems

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📏 Representing Knowledge Using Rule-Based Deduction Systems

Rule-based systems use if-then rules for knowledge representation and reasoning.

✅ Components:

• Rule Base: Contains facts and rules.
• Inference Engine: Applies rules to known facts to derive conclusions.

Example:

IF temperature > 100
THEN Display "High Temperature Warning"

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🤔 Reasoning Under Uncertainty

Many real-world situations involve uncertain or incomplete information. AI uses probabilistic methods to handle such cases.

✅ Key Techniques:

• Probability Theory
• Bayesian Inference
• Dempster-Shafer Theory

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📊 Review of Probability

Probability measures the likelihood of events.

Key Concepts:

• Random Variable: Represents possible outcomes.
• Probability Distribution: Assigns probabilities to events.

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📈 Bayes’ Probabilistic Inferences

Bayes’ theorem helps update the probability of a hypothesis based on new evidence.

Bayes’ Theorem Formula:

P(H|E) = [P(E|H) * P(H)] / P(E)

• P(H|E): Posterior probability (after evidence)
• P(E|H): Likelihood
• P(H): Prior probability
• P(E): Probability of evidence

Applications:

• Medical Diagnosis
• Spam Detection
• Risk Analysis

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🧩 Dempster-Shafer Theory

Dempster-Shafer Theory is used for reasoning with uncertainty without requiring prior probabilities.

Key Features:

• Combines evidence from different sources.
• Produces a belief function representing the degree of belief.

Applications:

• Sensor Fusion
• Fault Diagnosis
• Decision Support Systems

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📌 Conclusion

Knowledge representation and reasoning are critical for enabling AI systems to think, reason, and make decisions intelligently. Techniques such as predicate logic, semantic networks, frames, rule-based systems, and probabilistic reasoning empower AI to handle both certain and uncertain information effectively.