The document discusses sources and approaches to handling uncertainty in artificial intelligence. It provides examples of uncertain inputs, knowledge, and outputs in AI systems. Common methods for representing and reasoning with uncertain data include probability, Bayesian belief networks, hidden Markov models, and temporal models. Effectively handling uncertainty through probability and inference allows AI to make rational decisions with imperfect knowledge.

1. Uncertainty handling in
Artificial Intelligence

2. Uncertainty ?
• Lack of exact information
• Doubtful information
?

3. There are Six Tea Cups on top
of the table if one falls down and
how many are remaining?
5

4. Note
Most Intelligence Systems have some degree
of uncertainty associated with them

5. All birds Fly !!!
Most of the birds Fly !!!
95% of the birds Fly !!!

6. Sources of Uncertainty
• Uncertain Inputs
• Uncertain Knowledge
• Uncertain Outputs

7. To Solve Uncertainty
• How to Represent Uncertain Data?
• How to combine two or more pieces of Uncertain Data?
• How to draw inference using certain data?

8. Approaches to handling Uncertainty
• Default reasoning
• Worst- case reasoning
• Probabilistic reasoning

9. Methods for Managing Uncertain Information
• Probability
• Bayesian belief network
• Temporal Models
• Hidden Markov Models

10. The Wumpus World in Artificial intelligence
https://www.javatpoint.com/the-wumpus-world-in-artificial-intelligence

12. Real time example for uncertainty
• My car doesn’t break down
• Run out of gas
• I don’t get in to an accident
• There are no accidents on the bridge
• Plane doesn’t leave early

13. Example of rule for dental diagnosis
using
p Symptom(p, Toothache) ⇒ Disease(p, Cavity)
• This rule is wrong and in order to make it true we have
to add an almost unlimited list of possible causes:
• p Symptom(p, Toothache) ⇒ Disease(p, Cavity) ∨ Disease(p,
GumDisease) ∨ Disease(p, Abscess)…

14. What is the role of
probability and inference in AI?
• Many algorithms are designed as if knowledge is perfect, but it rarely is.
• There are almost always things that are unknown, or not precisely known.
• Examples: - bus schedule
- quickest way to the airport
- sensors
- joint positions
- finding an H-bomb
• An agent making optimal decisions must take into account uncertainty

15. Probability as frequency:
k out of n possibilities
• Suppose we’re drawing cards from a standard deck:
- P(card is the Jack ♥ | standard deck) = 1/52
- P(card is a ♣ | standard deck) = 13/52 = 1/4
• General probability of event given some conditions:
P(event | conditions)

16. Making rational decisions when faced with
uncertainty
• Probability
-the precise representation of knowledge and uncertainty
• Probability theory
-how to optimally update your knowledge based on new
information
• Decision theory: probability theory + utility theory how to use this
information to achieve maximum expected Utility

19. Basic Postulates by taking an Example