Presentation is loading. Please wait.

Presentation is loading. Please wait.

CS 4100 Artificial Intelligence

Similar presentations


Presentation on theme: "CS 4100 Artificial Intelligence"— Presentation transcript:

1 CS 4100 Artificial Intelligence
Prof. C. Hafner Class Notes March 22, 2012

2

3 Guidelines for AI Term Project Proposals
Should be a 2-4 page printable document, including a title for your project, name and members of your team (with addresses), the course, semester, date, etc. Introduce the domain and give some preliminary examples of concepts that will probably be important in your ontology. You should mention at least one taxonomy and one relationship that will need to be modeled. Justify that planning is useful/necessary in this domain. For example you might mention a simple (even trivial) kind of planning that needs to take place. Make a start at describing some planning goals/tasks that will require non-trivial knowledge and reasoning. Mention the planning technique you intend to apply to this (STRIPS operators or formal logic/Situation Calculus). It is not necessary to mention or discuss Protégé implementation.

4 Conditional Probability
P(X | A,B,C) defined as P(X,A,B,C) / P(A,B,C) Assuming Boolean Random Variables 8 values of the conditions A B C Adding up all P(X,A,B,C) gives us “prior” probability of X P(~X | A, B, C) = 1 – P(X | A, B, C)

5 Conditional Probability
Chain rules says: P(A, B, C) = P(A | B, C) P(B, C) = P(A | B, C) P(B | C) P(C) can also be written P(A) P(B | A) P(C | A, B) Bayes rules says: P(X | A,B,C) = P(A,B,C | X) P(X) / P(A,B,C) *We call α = 1/P(A,B,C) and it applies to all values of X And write: P(X | A,B,C) = α P(A,B,C | X) P(X)

6 Conditional Probability
This is interesting when there is some independence Conditional independence of 2 variables: P(X , Y | Z) = P(X | Z) P(Y | Z) Therefore if A, B, and C are conditionally independent of X INSTEAD OF P(X | A,B,C) = = α P(A,B,C | X) P(X) = α P(A |X) P(B | A, X) P(C | A, B, X) P(X) We get: P(X | A,B,C) = α P(A,B,C | X) P(X) = α P(A| X) P(B | X) P(C| X) P(X)

7 How to construct a Bayes Net

8

9

10

11

12

13

14 Test your understanding: design a Bayes net with plausible numbers

15 Calculating using Bayes’ Nets
You can construct JPTable but often that is overkill

16

17

18

19

20

21

22

23


Download ppt "CS 4100 Artificial Intelligence"

Similar presentations


Ads by Google