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Uncertainty


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



Overview/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.

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

Prerequisites
None

Expected Duration (hours)
0.8

Lesson Objectives

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
  • 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
  • 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
  • describe the Markov Decision Process and how it can be used by an intelligent agent
  • Course Number:
    sd_exai_a05_it_enus

    Expertise Level
    Intermediate