• Online, Self-Paced
Course Description

Many problems aren't fully observable and have some degree of uncertainty, which is challenging for AI to solve. In this course, you will learn how to make agents deal with uncertainty and make the best decisions.

Learning Objectives

Understanding Uncertainty

  • start the course
  • describe uncertainty and how it applies to AI
  • describe how probability theory is used to represent knowledge to help an intelligent make decisions

Understanding Utility Theory

  • describe utility theory and how an agent can calculate expected utility of decisions
  • describe how preferences are involved in decision making and how the same problem can have different utility functions with different agents
  • describe how risks are taken into consideration when calculating utility and how attitude for risks can change the utility function
  • describe the utility of information gain and how information gain can influence decisions

Examining the Markov Decision Process

  • define Markov chains
  • define the Markov Decision Process and how it applies to AI
  • describe the value iteration algorithm to decide on an optimal policy for a Markov Decision Process
  • define the partially observable Markov Decision Process and contrast it with a regular Markov Decision Process
  • describe how the value iteration algorithm is used with the partially observable Markov Decision Process
  • describe how a partially observable Markov Decision Process can be implemented with an intelligent agent

Practice: Markov Decision Process

  • describe the Markov Decision Process and how it can be used by an intelligent agent

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.