Uncertainty
Artificial Intelligence
| Beginner
- 13 videos | 39m 30s
- Includes Assessment
- Earns a Badge
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
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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
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1.What Is Uncertainty?3m 29sUP NEXT
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2.Uncertainty Representation5m 7s
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3.Utility Theory1m 18s
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4.Utility and Preferences3m 9s
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5.Utility and Risks3m 45s
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6.Value of Information2m 29s
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7.Markov Chains3m 20s
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8.Markov Decision Process2m 27s
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9.MDP Value Iteration2m 29s
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10.Partially Observable Markov Decision Process (POMDP)2m 46s
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11.POMDP Value Iteration3m 30s
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12.Applying POMDPs3m 7s
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13.Exercise: Describe the Markov Decision Process2m 35s
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