Uncertainty from Skillsoft

Online, Self-Paced

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