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Constraint Satisfaction Problems


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Search algorithms provide solutions for many problems, but they aren't always the optimal solution. This course will show you how constraint satisfaction algorithms are better than search algorithms in some cases, and how to use them.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Prerequisites
None

Expected Duration (hours)
0.5

Lesson Objectives

Constraint Satisfaction Problems

  • start the course
  • define constraint satisfaction problems and describe how they are different from search problems
  • list some examples of problems that are better for constraint satisfaction algorithms than search algorithms
  • describe how to use a backtracking search to solve a constraint satisfaction problem
  • describe how to order variables when performing a backtracking search
  • describe arc consistency and other types of constraint consistency in a constraint satisfaction problem
  • describe how to use arc consistency to solve a constraint satisfaction problem with constraint propagation
  • describe how to use the backjumping and forward checking inference method in a backtracking search
  • describe how local search algorithms can be used to solve constraint satisfaction problems
  • describe how to represent a Sudoku puzzle and how to solve it as a constraint satisfaction problem
  • build a full high-level representation and solution for a constraint satisfaction problem
  • Course Number:
    sd_exai_a03_it_enus

    Expertise Level
    Everyone