• Online, Self-Paced

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 NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development
  • Technology R&D

Specialty Areas have been removed from the NICE Framework. With the recent release of the new NICE Framework data, updates to courses are underway. Until this course can be updated, this historical information is provided to give better context as to how it can help you with your cybersecurity goals.

Feedback

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