# Uncertainty

Artificial Intelligence    |    Beginner
• 13 videos | 39m 30s
• Includes Assessment
Rating 4.3 of 63 users (63)
Many problems aren't fully observable and have some degree of uncertainty, which is challenging for AI to solve. Discover how to make agents deal with uncertainty and make the best decisions.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• Upon completion of this video, you will be able to describe uncertainty and how it applies to artificial intelligence.
• After completing this video, you will be able to describe how probability theory is used to represent knowledge to help an intelligent agent make decisions.
• 3.  Utility Theory
Upon completion of this video, you will be able to describe utility theory and how an agent can calculate expected utility of decisions.
• 4.  Utility and Preferences
Upon completion of this video, you will be able to describe how preferences are involved in decision making and how the same problem can have different utility functions with different agents.
• 5.  Utility and Risks
After completing this video, you will be able to describe how risks are taken into consideration when calculating utility and how attitude towards risks can change the utility function.
• 6.  Value of Information
After completing this video, you will be able to describe the usefulness of information gain and how information gain can influence decisions.
• 7.  Markov Chains
Find out how to define Markov chains.
• 8.  Markov Decision Process
In this video, you will learn about the Markov Decision Process and how it applies to AI.
• 9.  MDP Value Iteration
Upon completion of this video, you will be able to describe the value iteration algorithm and how it can be used to decide on an optimal policy for a Markov Decision Process.
• 10.  Partially Observable Markov Decision Process (POMDP)
In this video, you will learn how to define the partially observable Markov Decision Process and how it differs from a regular Markov Decision Process.
• 11.  POMDP Value Iteration
After completing this video, you will be able to describe how the value iteration algorithm is used with the partially observable Markov Decision Process.
• 12.  Applying POMDPs
Upon completion of this video, you will be able to describe how a partially observable Markov Decision Process can be implemented with an intelligent agent.
• 13.  Exercise: Describe the Markov Decision Process
Upon completion of this video, you will be able to describe the Markov Decision Process and how it can be used by an intelligent agent.

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

## YOU MIGHT ALSO LIKE

Rating 4.7 of 71 users (71)
Rating 4.7 of 21 users (21)

## PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Rating 4.3 of 79 users (79)
Rating 4.3 of 181 users (181)
Rating 4.5 of 1758 users (1758)