Artificial Intelligence - UNIT 3-Topic 2-knowledge representation issues

 

UNIT - III

Topic 2 :  Knowledge Representation Issues

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Part A: Introduction

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✅ What is Knowledge Representation?

In AI, Knowledge Representation (KR) refers to the method of encoding information about the world into a format that a computer system can use to solve problems and make decisions.

But representing knowledge is not always easy — several issues or challenges arise during this process. These are called Knowledge Representation Issues.

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Part B: Why are These Issues Important?

• A poorly designed KR system leads to incorrect or slow decision-making.
• Understanding these issues helps AI systems to become more reliable, intelligent, and flexible.

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Part C: Major Issues in Knowledge Representation

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 1. Representational Adequacy

Can the representation capture all kinds of knowledge needed for solving the problem?

• Some systems cannot represent time, uncertainty, or default values.
• Example: A simple rule like “Birds can fly” may not work for penguins or ostriches.

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 2. Inferential Adequacy

Can the system derive new knowledge from existing knowledge?

• An intelligent system must not just store facts but also reason from them.
• Example: If “All humans are mortal” and “Socrates is a human”, the system should infer “Socrates is mortal”.

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 3. Inferential Efficiency

Can the system perform reasoning quickly and effectively?

• It should be able to answer questions in real-time, especially in games, robotics, or medical AI.

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 4. Acquisition and Learning

How easy is it to add or learn new knowledge?

• Manually feeding data is difficult.
• AI should be able to learn automatically from new inputs or environments.

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 5. Handling Incomplete and Uncertain Knowledge

Can the system make decisions even when information is missing or unclear?

• Real-world data is often incomplete (e.g., unknown weather) or uncertain (e.g., possibility of rain).
• Solution: Use probabilities or fuzzy logic.

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 6. Expressiveness

Can the representation handle different types of knowledge (e.g., facts, relationships, rules)?

• Some tasks need temporal knowledge (time-based), spatial knowledge (location), or procedural knowledge (how to do things).

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 7. Ambiguity and Vagueness

Natural language or real-world data often contains ambiguous meanings.

• Example: “He saw the man with the telescope.” → Who has the telescope?
• The system must deal with multiple interpretations.

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 8. Scalability

Can the knowledge base grow and update without breaking?

• As more knowledge is added, the system must remain efficient and accurate.

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 9. Consistency

Does new knowledge conflict with existing knowledge?

• Example: If “All birds can fly” and later we say “Penguin is a bird and cannot fly”, this causes a conflict.

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 10. Context Dependence

Meaning of information can change depending on context.

• Example: The word “bank” could mean a river bank or a money bank depending on the sentence.

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Part D: How to Handle These Issues

Issue	Solution / Technique
Incompleteness & Uncertainty	Use probability, fuzzy logic, or Bayesian nets
Conflicts in rules	Use non-monotonic logic or default reasoning
Speed & efficiency	Use inference engines, heuristics, and pruning
Context & ambiguity	Use semantic networks, context-based parsing

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✅ Real-World Example: Medical Diagnosis System

Challenge	Solution
Patients show incomplete symptoms	Use probabilistic reasoning to guess likely disease
Conflicting symptoms	Prioritize with confidence scores or fuzzy values
Many new diseases appear	System must allow easy knowledge updates

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📝 Summary

• Knowledge Representation Issues are the challenges faced while designing systems that can store and reason with knowledge.
• Major issues include: incompleteness, ambiguity, learning difficulty, reasoning speed, scalability, and uncertainty.
• By addressing these issues, AI becomes more accurate, intelligent, and useful in real-world situations.

"It’s not just what the AI knows – it’s how well it understands, uses, and learns from it that defines its intelligence."