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AI · Theory

Constraint Satisfaction Problems

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Theory

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Constraint Satisfaction Problems

CSP: variables, domains, constraints. Sudoku, map colouring, timetables. Solve by search + constraint propagation (arc consistency). 8-queens is the textbook CSP: one queen per row, no shared column/diagonal.

Don’t start with genetic algorithms. First: variables + domains + constraints, then backtracking.

Constraint Satisfaction Problems — output: A= R B= G and A= G B= R. Neighbours differ. Sudoku is the same idea bigger.

Diagram
data → train / test
           │
           ▼
         model
           │
           ▼
        predict
Exam tip

Define CSP + one example.

Example

# CSP — 2-node map
# tiny map colour: A-B neighbours, 2 colours
colours = ["R", "G"]
for a in colours:
    for b in colours:
        if a != b:
            print("A=", a, "B=", b)

Constraint Satisfaction Problems — output: A= R B= G and A= G B= R. Neighbours differ. Sudoku is the same idea bigger.

Short notes

  • DefVariables, domains, constraints.
  • RuleMap colour / Sudoku / 8-queens.

Questions

1

Explain Constraint Satisfaction Problems as if you are teaching a junior — definition, then one example.

2

What does the example print, and what does that prove?

3

What mistake do freshers make with Constraint Satisfaction Problems?

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