AVL Tree with explanation
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Unit 3. Knowledge Representation & Reasoning
Artificial Intelligence unit 3 – Knowledge Representation & Reasoning, most Important, most asked previous year questions in your semester exams are listed below.
It will help you in the preparation of your semester exam to score good marks. It will also save you from the backlogs.
Topic : Knowledge Representation & Reasoning (Key Note and Questions)
Key Note:
Questions
- Q1). Briefly describe the meaning of knowledge representation and knowledge acquisition . What procedure is followed for knowledge acquisition? Explain.
- Q2). Write a property and approaches of a knowledge representation scheme.
- Q3). Define the term logic. Explain its types also.
- Q4). Prove that following sentence is valid: " If price fall then sell increases. If sell increases then Jhon makes the whole money. But John doesn't make the whole money. Therefore , price do not fall".
- Q5). Explain inference rules with example.
- Q6). Consider the argument ," All dogs bark. Some animals are dogs. Therefore, some animals bark". Determine whether the conclusion is a valid consequence of the premises.
- Q7). Translate the following sentence into formulas in predicate logic and clause form: a). John likes all kind of food. b). Apples are food. c). Chicken is food. d). Anything any one eats and is not killed by is food. e). Bill eats peanuts and is still alive. f). Sue eats everything Bill eats.
- Q8). Determine whether the following argument is valid ."if I work whole night on this problem ,then I can solve it. If I solve the problem, then I will understand the topic. therefore , I will work whole night on this problem, then I will understand the topic."
- Q9). Prove that follwing statement are inconsitent. 1. John loves Mary and Reddy is not happy but her parents are happy. 2. If Jhon marries Marry then william and her friend Reddy will be happy. 3. John will Marry if Marry loves John.
- Q10). Describe frame. What is Minskey frame system theory?
Topic : Forward & Backward chaining and Resolution (Key Note and Questions)
Key Note:
Questions
Topic : Probabilistic reasoning, utility theory, HMM, and Bayesian Networks (Key Note and Questions)
Key Note:
Questions
- Q1). What is probabilistic reasoning? Also describe the role HMM in probabilistic reasoning.
- Q2). What is utility Theory?
- Q3). Describe the process of natural deduction for investigating the validity of an argument . Explain your answer by choosing a suitable example.
- Q4). Define Hidden Markov Model(HMM). Illustrate how HMMs are used for speech recognition.
- Q5). Describe Bayesian networks. How are the Bayesian networks powerful representation for uncertainty knowledge.
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