moduledeeplearning Reasoning_Methods.pptx

Artifical Intelligence

1. Reasoning Methods (Contd..)

2. Mid Term Exam – in next 2-3 weeks! Module 2 – Up to Date PPT’s have been uploaded to Collpoll !

3. Inductive Reasoning Deductive Reasoning SPECIFIC argument GENERAL conclusion Logically True May or may not be realistically TRUE GENERAL argument SPECIFIC conclusion Logically True Realistically TRUE Inductive reasoning is a logical approach where you progress from specific ideas to general conclusion Deductive reasoning is a logical approach where you progress from general ideas to specific conclusion

4. Deductive Reasoning Logically True Realistically TRUE Example: Statement 1: All mangoes are fruits [General statement] Statement 2: All fruits have seeds [General statement] Conclusion: Mangoes have seeds [Specific Conclusion]

5. Deductive Reasoning Logically True Realistically TRUE Example: Statement 1: All mangoes are fruits [General statement] Statement 2: All fruits have seeds [General statement] Conclusion: All fruits have seeds [Specific Conclusion] Logically True May or may not be realistically TRUE Example: Statement 1: Mango is a Fruit [Specific statement] Statement 2: The box is full of Fruits [Specific statement] Conclusion: The box is full of Mangoes [General Conclusion] Inductive Reasoning

6. Inductive Reasoning Examples: S1 = Rajesh loves Reading [Specific] S2 = Rajesh is a good student [Specific] Conclusion = Good students love reading [General]

7. Few more examples for you to solve: Rusty is a shelter dog. He is happy. All shelter dogs are happy. Rusty is a shelter dog. All shelter dogs are happy. Therefore, he is happy My father is Filipino. Everyone from Philippines has dark hair. Therefore my father has a dark hair All dogs have ears. Golden retrievers are dogs. Therefore, they have ears All athletes work out in the gym. Bolt is an athlete. Therefore Bolt works out in the gym My father is Filipino. He has dark hair. Therefore, everyone from Philippines have dark hair. 1. I 2. D 3. D 4. D 5. D 6. I

8. Inductive Reasoning Observation: A student notices that their concentration improves after a 10-minute meditation before studying. Pattern: This improvement is consistent over a month of daily observation. Hypothesis: The student hypothesizes that meditation positively affects their concentration levels. Theory: After researching and finding similar experiences reported by peers, the student theorizes that short meditation sessions lead to better concentration for studying

9. Deductive Reasoning Theory: A teacher explains the theory that students learn better when teaching a concept to someone else. Hypothesis: A student hypothesizes that if they teach a math formula to a classmate, both will be able to solve equations more accurately. Observation: They observe their self and other classmate's accuracy rates before and after the teaching session Confirmation: They find that both of their success rates improve, confirming the hypothesis and supporting the initial theory.

10. Inductive Reasoning Deductive Reasoning SPECIFIC argument GENERAL conclusion Logically True May or may not be realistically TRUE GENERAL argument SPECIFIC conclusion Logically True Realistically TRUE Inductive reasoning is a logical approach where you progress from specific ideas to general conclusion Deductive reasoning is a logical approach where you progress from general ideas to specific conclusion top-down approach Bottom - up approach

11. Abductive Reasoning OBSERVATION OBSERVATION BEST GUESS – SPECIFIC CONCLUSION / PREDICTION

12. Abductive Reasoning Sam and Jack are close friends They got into big fight and weren’t speaking BEST GUESS – SPECIFIC CONCLUSION / PREDICTION = They must be friends again Example: I know that Sam and Jack are close friends, though I heard they got into a big fight and weren’t speaking. I saw them on a walk together the other day. I guess they must have made up. Saw them on a walk together other day

13. Abductive Reasoning My partner gets home from work at around 6 It’s now 7 o’clock BEST GUESS – SPECIFIC CONCLUSION / PREDICTION = She must be stuck in bad traffic Example:   Usually, my partner gets home from work at around 6. It’s now 7 o’clock, so she must be stuck in bad traffic. .

14. "If all mammals have a backbone and dolphins are mammals, then dolphins must have a backbone.“ "The cake recipe says to bake for 25 minutes, but it's already burnt at 20 minutes. Perhaps my oven runs hotter than the recipe's intended oven.“ "Gravity makes things fall to the ground. If I drop my pencil, it will fall to the ground.“ "The patient has symptoms of fever and a sore throat. The most common illness with these symptoms is the flu. Therefore, the patient might have the flu.“ "This fossil has characteristics of both an aquatic and a land animal. This could suggest that it's from a species that was transitioning from water to land living." 1.D 2. A 3. D 4. A 5. A

15. Common Sense Reasoning Common sense reasoning is the ability to reason about the world based on their understanding of everyday concepts and experiences that they have gained throughout their lives.

16. Why is Common Sense Reasoning Important in AI?

17. Challenges of Common Sense Reasoning in AI M achines do not gain common sense through lived experiences The vastness and continual evolution of common sense knowledge pose significant challenges. AI needs continuous learning to integrate new and evolving common sense knowledge into reasoning Common sense knowledge is often implicit, making it difficult to define and teach to AI.

18. Approaches to Common Sense Reasoning Rule – Based System Semantic Networks The AI assistant is tasked with scheduling a meeting for a user but notices that the user already has a dentist appointment at the proposed meeting time. Common sense reasoning: Understanding Time Overlaps: Recognizing that two events cannot physically occur at the same time for the user. Priority Inference: Inferring which types of events might be more flexible or important. Alternative Suggestion: Based on common sense and learned user preferences, the AI suggests rescheduling the meeting to a free slot. Machine Learning

19. Do you think it will Rain in Bangalore today evening?

20. If it is raining (A), then the ground is wet (B)

21. If number 1 is detected (A), then you win (B)

22. Statement: if A is true then B is true Think of a situation: we are not sure about whether A is true or not   we cannot express this statement, this situation is called UNCERTAINTY

23. Causes of uncertainty: Following are some leading causes of uncertainty to occur in the real world. Information occurred from unreliable sources. Experimental Errors Equipment fault Temperature variation Climate change.

24. PROBABILITY  the extent to which something is likely to happen

25. PROBABILISTIC REASONING Probabilistic reasoning is a way of knowledge representation where we apply the concept of probability to indicate the uncertainty in knowledge

26. In probabilistic reasoning, we combine probability theory with logic to handle the uncertainty.

27. PROBABILITY  the extent to which something is likely to happen 0 ≤    ≤ 1  where P( ω ) is the probability of an event A P(A) = 0,  indicates total uncertainty in an event A. P(A) = 1,  indicates total certainty in an event A. ω P( )

28. 0 ≤ P( ω ) ≤ 1

29. Imagine you have a fair, six-sided die, and you want to calculate the probability of rolling a 4? Number of desired outcomes (rolling a 4) = 1 Total number of outcomes = 6

30. What is the probability of getting heads three times in a row when flipping a coin? Let’s look at the possible outcomes if we flipped a coin three times. Let H=heads and T=tails. The possible outcomes are: { HHH, THH, THT, HTT, HHT, HTH, TTH, TTT } Each of these outcomes has a probability of ⅛. the probability of flipping a coin three times in a row and having it land on heads all three times is ⅛

31. What is the probability of getting heads two times in a row when flipping a coin three times? Let’s look at the possible outcomes if we flipped a coin three times. Let H=heads and T=tails. The possible outcomes are: { HHH, THH, THT, HTT, HHT, HTH, TTH, TTT } Each of these outcomes has a probability of ⅛. the probability of flipping a coin three times in a row and having it land on heads two times in a row is 3/8

32. What is the probability of flipping a coin three times and getting at least one head? 7/8

33. 5 volunteers Please

34. Monty Hall Problem

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