🧠 ARTIFICIAL INTELLIGENCE (AI) – CIE IGCSE COMPUTER SCIENCE NOTES
1️⃣ What is Artificial Intelligence?
🔹 Definition
Artificial Intelligence (AI) is a branch of computer science that deals with the simulation of intelligent behaviour by computers.
In simple terms:
AI allows computers to perform tasks that normally require human intelligence.
Examples of intelligent behaviour:
- Making decisions
- Solving problems
- Recognising patterns
- Learning from data
- Understanding speech
2️⃣ Main Characteristics of AI
According to the syllabus, AI involves:
✅ 1. Collection of Data
AI systems require large amounts of data.
This data:
- Is stored in a database or knowledge base
- Is used to make decisions
- Can be updated over time
Example:
- A medical AI system stores information about diseases and symptoms.
✅ 2. Rules for Using the Data
AI systems use rules to process and interpret data.
Rules are usually in the form:
IF condition THEN action
Example:
- IF temperature > 39°C THEN possible fever.
✅ 3. Ability to Reason
AI systems can:
- Analyse information
- Apply logical rules
- Make conclusions
This simulates human reasoning.
Example:
If:
- Patient has high temperature
- Patient has sore throat
Then: - AI may conclude possible infection.
✅ 4. Ability to Learn and Adapt (Machine Learning)
Some AI systems improve automatically.
They:
- Detect patterns
- Adjust their processes
- Update their internal data
This is called machine learning.
3️⃣ Types of AI (Limited to Syllabus)
The syllabus focuses only on:
- Expert Systems
- Machine Learning
👨⚕️ EXPERT SYSTEMS
🔹 Definition
An expert system is an AI system that mimics the decision-making ability of a human expert.
It is designed to:
- Solve problems
- Give advice
- Make decisions in a specific field
Example fields:
- Medicine
- Engineering
- Finance
🔹 Components of an Expert System
You MUST know these four components for the exam:
1️⃣ Knowledge Base
Stores facts and information.
Contains:
- Data
- Facts
- Information about a subject area
Example:
In a medical system:
- List of symptoms
- List of diseases
- Medical facts
2️⃣ Rule Base
Contains decision rules.
Usually written as:
IF condition THEN action
Example:
IF cough AND fever THEN possible flu
3️⃣ Inference Engine
This is the “thinking” part.
It:
- Applies the rules to the knowledge base
- Draws conclusions
- Produces decisions
Very important term for exams.
4️⃣ User Interface
Allows communication between:
- User
- Expert system
The user:
- Inputs data (e.g., symptoms)
- Receives advice/output
🔹 How an Expert System Works (Step-by-Step)
- User enters data.
- System checks knowledge base.
- Inference engine applies rules.
- System produces advice or decision.
- Output shown through interface.
🔹 Advantages of Expert Systems
✅ Available 24/7
✅ Consistent decisions
✅ Reduces human error
✅ Useful where human experts are scarce
🔹 Disadvantages of Expert Systems
❌ Expensive to develop
❌ Limited to stored knowledge
❌ Cannot think creatively
❌ May give incorrect advice if data is wrong
🤖 MACHINE LEARNING
🔹 Definition
Machine learning is when a program has the ability to automatically adapt its own processes and/or data.
In simple terms:
The system learns from experience without being reprogrammed.
🔹 How Machine Learning Works (Basic Level)
- Large amounts of data are collected.
- Patterns are detected.
- The system adjusts its internal rules.
- Performance improves over time.
🔹 Examples of Machine Learning
- Email spam filters
- Speech recognition
- Recommendation systems
- Image recognition
Example:
A spam filter:
- Learns which emails are spam.
- Updates rules automatically.
🔹 Key Difference
| Expert System | Machine Learning |
|---|---|
| Uses fixed rules | Learns from data |
| Knowledge manually added | Improves automatically |
| Based on human expertise | Based on pattern recognition |
This comparison is often tested.
4️⃣ Basic Operation of AI Systems
AI systems generally follow this structure:
🔹 Input
Data is collected:
- User input
- Sensors
- Databases
🔹 Processing
System:
- Applies rules
- Uses inference engine
- Analyses patterns
🔹 Output
System produces:
- Advice
- Prediction
- Decision
🔹 Feedback (Machine Learning Only)
System:
- Adjusts rules
- Improves performance
🔑 Important Key Terms
| Term | Definition |
|---|---|
| Artificial Intelligence | Simulation of intelligent behaviour by computers |
| Expert System | AI system that mimics a human expert |
| Knowledge Base | Stores facts and information |
| Rule Base | Contains IF-THEN rules |
| Inference Engine | Applies rules to make decisions |
| Machine Learning | System that adapts automatically using data |
✏️ Exam Tips (Very Important)
🔹 Tip 1: Always Use Technical Vocabulary
Markers look for:
- Knowledge base
- Rule base
- Inference engine
- Machine learning
- Adapt
- Simulate intelligent behaviour
🔹 Tip 2: For “Explain” Questions
Do not just define.
Bad:
AI is computers acting smart.
Good:
AI is the simulation of intelligent behaviour by computers, allowing systems to analyse data, apply rules and make decisions similar to a human.
🔹 Tip 3: For 6–8 Mark Questions
Structure your answer clearly:
- Define AI
- Explain components
- Give examples
- Mention advantages/disadvantages
🔹 Tip 4: If Asked to Describe Components of Expert System
You MUST mention all four:
- Knowledge base
- Rule base
- Inference engine
- Interface
Missing one loses marks.
⚠️ Common Exam Mistakes
❌ Saying AI “thinks like a human” (too vague)
❌ Forgetting inference engine
❌ Mixing expert systems and machine learning
❌ Writing about robots instead of AI
📌 Quick Revision Summary
AI:
- Simulates intelligent behaviour.
- Collects and processes data.
- Uses rules to reason.
- Can learn and adapt (machine learning).
Two types:
- Expert Systems (knowledge base + rule base + inference engine + interface)
- Machine Learning (self-adapting systems)