1. Define AI

Artificial Intelligence (AI) is a field of computer science that creates machines which can think, learn, and make decisions like humans by analyzing data and solving problems.

Example (Steps):

  1. Input data (voice command)
  2. AI processes it
  3. Gives output (answer/action)

2. Problem Solving Process

The problem solving process includes defining the problem, collecting data, selecting a method, generating solutions, evaluating them, and improving the final solution.

Example (Steps):

  1. Understand problem (low marks)
  2. Collect data (previous marks)
  3. Choose method (study plan)
  4. Apply solution (study)
  5. Evaluate result

3. Knowledge Representation Techniques

Logical representation uses rules and statements, while semantic networks use diagrams (nodes and links).

Example (Steps):

Logical → “All humans are mortal”
Semantic → Human → is a → living being

4. Knowledge-Based Agents

They store facts and rules in a knowledge base and use reasoning to choose the best action.

Example (Steps):

  1. Input: Fever + cough
  2. Check rules
  3. Match symptoms
  4. Output: Suggest disease

5. Conditions of Unification

Unification works when predicates match, arguments are same, and substitutions are consistent.

Example (Steps):

  1. P(x, y)
  2. P(a, b)
  3. Replace → x=a, y=b
  4. Match successful

6. Applications of AI Planning

AI planning is used in robotics and scheduling systems.

Example (Steps):

  1. Define task (clean room)
  2. Plan actions
  3. Execute steps
  4. Complete task

7. Partial Order Planning

Partial order planning arranges actions only when necessary, allowing flexible execution.

Example (Steps):

  1. Goal: Make breakfast
  2. Actions: boil water, toast bread
  3. No strict order
  4. Tasks can be done in any sequence

8. Planning Under Limited Resources

It means planning when resources like time or money are limited.

Example (Steps):

  1. Budget = ₹1000
  2. List needs
  3. Prioritize important items
  4. Spend wisely

9. Bayes’ Theorem

Bayes’ theorem is used to update probability using new evidence.

Example (Steps):

  1. Prior: Chance of disease
  2. Evidence: Test result
  3. Apply formula
  4. Get updated probability

10. Conditional Independence

Conditional independence means two variables become independent when a third variable is known.

Example (Steps):

  1. Rain affects traffic
  2. Weather known
  3. Traffic independent of other factors
  4. Calculation becomes easier