Web & Social Media Analytics Previous Year Question Paper.pdf
Wk8of2
1. The Inference Engine Week 8 – 2 nd Lecture October 12, 2011 -21
2.
3.
4.
5.
6.
7. Deductive Reasoning (cont.) Example 1 Major Premise: All birds have wings Minor Premise: Tweety is a bird Conclusion : Tweety has wings Example 2 Major Premise: I do not go to work on public holidays Minor Premise: Tomorrow is Wesak day and it is a public holiday Conclusion : I will not go to work tomorrow October 12, 2011 -21
8.
9.
10. Example Premise: I saw a bird in Zoo Negara which can fly within the interior of the cage Premise: I heard the sound of an explosion. All the birds sitting on the nearby tree flew towards the sky Conclusion: All birds in my world can fly October 12, 2011 -21 Inductive Reasoning (cont.)
11.
12. Example: Query: What is the working hours of Engineers? The computer finds the analogy between engineers and white-collar employees It is known that white-collar employees work from 9 to 5. Hence by analogy, Engineers work from 9 to 5 October 12, 2011 -21 Analogical Reasoning
13.
14.
15. Inferencing with Rules Mainly involves use of modus ponens Most of the commercial Expert Systems use rules in inferencing uses modus ponens e.g.:- RULE 1: IF international conflict begins THEN the price of gold goes up Assume that the ES knows international conflict just started. This info. is stored in the facts(assertion) part of the knowledge base. Ie. the premise(IF) part of the rule is TRUE October 12, 2011 -21
16.
17.
18.
19. Forward and backward Chaining: Example Consider the following scenario: Suppose you want to fly from Denver to Tokyo and there are no direct flights between these two cities. Therefore, you try to find a chain of connecting flights starting from Denver and ending in Tokyo. There are two ways you can search for this chain of flights October 12, 2011 -21
20. Backward chaining Start with all flights that arrive at Tokyo and find the city where each flight originated. Then look up all the flights arriving at those cities and find where they originated. Continue this process until you find Denver. Here you are working backward from your goal(Tokyo) and hence is a goal-driven approach. October 12, 2011 -21
21. Forward Chaining List all flights leaving Denver and mark their destination(intermediate) cities. Then look up all the flights leaving these intermediate cities and find where they land; continue this process until you find Tokyo. In this case you are working forward from Denver toward your goal(Tokyo). So this is a data driven approach. October 12, 2011 -21
![Inferencing ,[object Object],[object Object],[object Object],[object Object],October 12, 2011 -21](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)