• Online, Self-Paced
Course Description

Many problems faced by intelligent agents can be solved using searching methods. This course will provide you with a definition for search problems and useful methods to solve these problems.

Learning Objectives

Introducing Search Problems

  • start the course
  • define search problems and how these can be used by AI agents
  • list some problems that are ideal for searching algorithms
  • define how to represent search problems

 

Brute Force Searching

  • describe the breadth-first search algorithm
  • describe the depth-first search algorithm
  • describe depth-limited search and the iterative deepening search algorithms

 

Informed Searching

  • describe the greedy approach for best-first informed searching
  • define heuristics and their various properties
  • describe how to create a good heuristic function for a given search problem
  • describe the A* search algorithm

 

Local Searching

  • describe local searching and the hill-climbing search algorithm
  • describe the simulated annealing search algorithm and how it improves on hill-climbing search

 

Practice: Identifying Search Problems

  • describe the three environmental characteristics of search problems, state the function for a consistent heuristic, and state the function for an A* search

 

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.