Artificial Intelligence - Unit Wise Questions

1. How turing Test is used to evaluate intelligence of a machine? What properties a machine should have to pass the Total turing test?[4+2]

1.  What is Artificial Intelligence (AI)? Describe your own criteria for computer program to be considered intelligent.

1.  What is ‘Turing Test’ in Artificial Intelligence (AI)? Criticize the performance of the ‘Turing Test’ to measure the intelligence of the machine.

1.  Define with suitable supporting statements and examples, “Artificial Intelligence is the system that act like humans”.

1.  Do you agree “the development of Artificial Intelligence has had some negative effect on the society”? If you agree list some of them and put your opinion in the support of development of Artificial Intelligence.

1. How the dimensions like thinking humanly and thinking rationally are used to evaluate intelligence behavior of a machine.

1.  How can you define AI from the dimension of rationality?

1.  Define Artificial Intelligence (AI). Explain the behaviors of the AI. What do you mean by Turing Test? Explain it.

2.  What is ‘Turing Test in AI? Criticize the performance of the ‘Turing Test’ to measure the intelligent of the machine.

2.  Justify that “System that think rationally” and “System that act rationally” are the part of artificial intelligence. Explain it with practical examples.

2.  “System that think like humans” and “System that act like humans” are the part of artificial intelligence. Justify that statement with practical examples.

4. What is Turing Test? How it can be used to measure intelligence of machine?

4.  What is Ai? How can you define AI from the perspective of thought process?

10. How philosophy, sociology and economics influence the study of artificial intelligence?

1.  What do you mean by rational agents? Are the rational agents intelligent? Explain.

2.  For each of the following agents, determine what type of agent architecture is most appropriate (i.e. table lookup, simple reflex, goal-based or utility based).

a. Medical diagnosis system

b. Satellite image analysis system

c. Part-pricking robot

d. Refinery controller

2.  For each of the following agents, determine what type of agent architecture is most appropriate (i.e., table lookup, simple reflex, goal-based or utility-based).

a. Medical diagnosis system

b. Satellite imagine analysis system

c. Part-picking robot

d. Refinery controller

2.  What are rational agents? How episodic task environment differs from sequential task environment? Support your answer with suitable examples.

2. What are intelligent agents? Differentiate Model Based Agents differ from utility Based Agents differ from utility Based Agent. Mention suitable examples of each.[1+5]

2.  What is intelligent agent? Design PEAS framework for,

        - Soccer playing agent

        - Internet shopping assistant

5.  Discuss the types of environment where an agent can work on.

5. How agent can be configured using PEAS framework? Illustrate with example.

1.  Define backward chaining. Explain the importance of backward chaining with two practical examples.

1. Construct a state space with appropriate heuristics and local costs. Show that Greedy Best First search is not complete for the state space. Also illustrate A* is complete and guarantees solution for the same state space.

1.   What do you mean by forward chaining? Why it is required? Explain it with two practical examples.

1. How informed search are different than uniformed? Given following state space, illustrate how depth limited search and iterative deepening search works? Use your own assumption for depth limit.

Hence, S is start and K is goal.        (3+7)

2.  Explain the uninformed search techniques with example.

3.  What is state space representation of problem? Represent the root finding problem having four cities in to state representation (you can choose any ordering of cities and links) and devise the complete problem formulation.

3.  In problem solving, why problem formulation must follow goal formulation? How state space representation can be used to solve a problem? Support your answer with an example.

3.  If we set the heuristic function h(n)=g(n) for both greedy as well A*. What will be effect in the algorithms? Explain?

3.  Consider the following graph, steps cost is given on the arrow: Assume that the successors of a state are generated in alphabetical order, and that there is no repeated state checking. A is the starting node and C is goal node.

a. Of the four algorithms breadth-first, depth-first and iterative-deepening, which find a solution in this case?

b. Write sequence of node expanding by algorithm if finds solution.

3. In problem solving, what is the concept of state space, state, successor function, goal test and path cost? Illustrate each with suitable example.[6]

3.  Justify the searching is one of the important part of AI. Explain in detail about depth first search and breadth first search techniques with an example.

4. Consider the search space below, where S is start state and G₁ and G₂ are goal state. The arcs are labelled with step cost. Given the heuristic by H(~) for each nodes. Now use iterative depending and greedy best first search for finding the goal state, Also determine which goal state is reached first in each case.[6]

4.  How uniform cost search works? Given following state-space, use uniform cost search algorithm to find the goal. Show each of iterations.

        Here S is start state and G is goal state.

4.  What is heuristic information? Suppose that we run a greedy search algorithm with h(n) = – g(n) and h(n) = g(n). What sort of search will the greedy search follow in each case?

4.  The minimax algorithm returns the best move for MAX under the assumption that MIN play optimally. What happens when MIN plays suboptimally?

4.  Consider the following map of French cities:

Apply the A* algorithm to find out a route from Bordeaux to Grenoble. The value v associated with a route between two neighboring cities M and N is the length (in kilometers) of that route. The value [w] associated with a city M is the straight line distance between M and Grenoble. Your solution should show each step of the algorithm.

4.  What is meant by admissible heuristic? What improvement is done in A* search than greedy Search? Prove that A* search gives us optimal solution if the heuristic function is admissible.

4.  How iterative depending search is better than DFS and BFS. Consider following state space, use iterative deepening search considering S as start and g as goal.

5.  Searching is an important part of AI, justify it. Explain any two types of blind search with suitable examples. How can you expand it to informed search?

5.  Justify that searching is one of the important part of AI. Explain in detail about depth first search and breadth first search techniques with an example.

5.  What is the need of alphabeta pruning in game search? Given following search space with utility, perform mini-max search and identify alpha-beta cutoff if any. Play from perspective of max player first.

5. How searching is done in adverserial search? Given following search space with utility values perform minimax search for max player and identify the possible alpha/beta cutoff.[1+5]

5.  Justify that AI can’t exist without searching. Explain in detail about any two types of informed search with practical examples.

6.  Illustrate with an example, how uniform cost search algorithm can be used for finding goal in a state space.

9.  Consider a following state space representing a game. Use minimax search to find solution and perform alpha-beta pruning, if exists.

11. Given following search space, determine if these exists any alpha and beta cutoffs.

2.  What is Bayes’s theorem? Explain its applications.

2. Consider following facts:

Every traffic chases some driver. Every driver who horns is smart. No traffic catches any smart driver. Any traffic who chases some driver but does not catch him is frustated.

Now configure FOPL knowledge base for above statements. Use resolution algorithm to draw a conclusion that "If all drivers horn, then all traffics are frustated".            (3+2+5)

2.  Why disjunctive normal form is required? Explain all the steps with examples.

2.  How resolution algorithm is used in FOPL to infer conclusion? 

Consider the facts;

    Anyone whom pugu loves is a star. Any  hero who does not reherse does not act. Anmol is a hero. Any hero who does not work does not reherse. Anyone who does not act is not a star. Convert above into FOPL and use resolution to infer that "If Anmol does not work, then pugu does not love Anmol".

3.  “A person born in Nepal, each of whose parents is a Nepali citizen by birth, is a Nepali citizen by birth. A person born outside Nepal, one of whose parents is a Nepali citizen by birth, is a Nepali citizen by decent. Several developed countries have dual citizenship provision, but Nepal doesn’t have that provision.” Represent the above sentences in first-order logic and explain each step.

3. Convert following statement into FOPL, every friend of Ramesh has visited pokhara. Everyone who visits Pokhara does boating on Fewalake. Ramesh has done boating on Fewalake. Now using resolution try to infer; some friend of Ramesh has done boating on Fewalake.

3.  Why normal forms are required in AI? How do you convert to the disjunctive normal form? Explain all the steps with practical examples.

3.  How do you convert to conjunctive normal form? Explain all the steps with examples.

4.  Differentiate between inference and reasoning. Why probabilistic reasoning is important in the AI? Explain with an example.

4.  “A deductive system is sound if any formula that can be derived in the system is logically valid. Conversely, a deductive system is complete if every logically valid formula is derivable. All of the system discussed in this article are both sound and complete. They also share the property that it is possible to effectively verify that a purportedly valid deduction is actually a deduction; such deduction systems are called effective”. Represent the above sentences in first-order logic and explain each step.

4.  “A key property of deductive systems is that they are purely syntactic, so that derivations can be verified without considering any interpretation. Thus a sound argument is correct in every possible interpretation of the language, regardless whether that interpretation is about mathematics, economics, or some other area. The artificial intelligence deals with deductive system soundly”. Represent the above sentences in first-order logic and explain each step.

5.  State whether the following sentences are valid, unsatisfiable, or neither.

a. Smoke => Smoke

b. Smoke => Fire

c. (Smoke => Fire) => (~Smoke=>~Fire)

d. Smoke V Fire V ~Fire

5.  Translate the following sentence into first order logic:

i. “Everyone’s DNA is unique and is derived from their parents’ DNA”.

ii. “No dog bites a child of its owner”.

iii. “Every gardener likes the sun”.

iv. “All purple mushrooms are poisonous”.

5.  Briefly describe the approaches of knowledge representation with example.

All cats like fish, cats eat everything they like, and Ziggy is a cat.

        a)  Translate the sentences into FOL.

        b)  Convert the sentences into clausal normal form.

        c)  Answer using FOL, if Ziggy eats fish?

5.  What is script? How knowledge is represented in script? Illustrate component of script with a example.

6.  Differentiate between inference and reasoning. Why probabilities reasoning is important in AI? Explain with an example.

6.  Consider the following sentence:

[(food => party) V (drinks => party)] => [(food ^ drinks) => party]

a. Convert the right hand and left hand sides of main implication into CNF.

b. Prove the validity of sentence using resolution.

6.  Represent the following sentences into a semantic network.

Birds are animals.

Birds have feathers, fly and lay eggs.

Albatross is a bird.

Donald is a bird.

Tracy is an albatross. 

6.  Consider the knowledge base:

“If it is hot and humid, then it is raining. If it is humid, then it is hot. It is humid”

a. Describe a set of propositional letters which can be used to represent the knowledge base.

b. Translate the KB into propositional letters using your propositional letters from part a.

c. Is it raining? Answer this question by using logical inference rule with KB.

6.  Consider the following a production system characterized by

    - Initial short term :          C5, C1, C3

    - Production rules:           C1 & C2 àC4

                                                         C3 àC2

                                                        C1 & C3 àC6

                                                        C4 àC6

                                                         C5 àC1

Show a possible sequence of two recognize-art cycles. Which will be the new content of the short-term memory after these two cycles?

6.  Define knowledge representation system. How knowledge is represented using semantic networks? Illustrate with an example.

7.  What do you mean by causal network? Explain it with practical application.

7. How Knowledge is represented using scripts? Support your answer with suitable example.[6]

7.  Convert the following sentence into predicate logic.

a. “No dog bites a child of its owner”?

b. “No two adjacent countries have the same color”?

7.  What do you mean by knowledge representation? Explain the characteristics of representation.

7.  What do you mean by reasoning in belief network? Explain it with example.

6. Construct sematic network for following facts:

        Ram is a person. Person are humans. All humans have nose. Humans are instarces of mammals. Ram has weight of 60 kg. Weight of Ram is less than weight of Sita.

7.  What do you mean by casual network? Explain it with practical application.

7.  What is Bayes’ theorem? Explain its applications.

7.  What is Bayesian network? Explain how Bayesian network represent and inference the uncertain knowledge.

8.  Define the Model-Based and Cased Based system. Discuss which system is suitable for the following problems.

a. Electronic Circuit Testing

b. Legal Reasoning

c. Disease Recognition

8.  What are conceptual graphs? Represent the following statements into conceptual graph.

“King Ram marry Sita, the daughter of king Janak”. 

8.  Why disjunctive normal form is required? Explain all the steps with examples.

8. Consider the following statements:

        Rabin likes only easy courses. Science courses are hard. All courses in the CSIT are easy. CSC 101 is a CSIT course.

a. Translate the sentences into predicate logic.

            b. Convert your sentences into clausal normal form (CNF).

7.  Define frame. How knowledge is encoded in a frame? Justify with an example.

9.  What are conceptual graphs? Represent the following statements into conceptual graph.

King Ram marry Sita, the daughter of king Janak.

9.  What do you mean by marginalization in probability distribution? Consider in Nepal, 51% of adults are males and rest are females. Consider one adult is randomly selected for a survey of drinking alcohol. It is found that 15% of males drink alcohol where as 2% of female drink alcohol. Now find the probability that the selected adult is a male.

9. What is the use of Baye's rule? Consider in a school, the number of boys student is 46% and that of girl student is 54%. Suppose 4% of boys student are over 5 feet tall and 2% of girls student are over 5 feet tall. If  a student is selected at random from among all those over 5 feet tall, what is the probability that the student is girl?[1+5]

9.  What is the difference between symbolic and non-symbolic AI? Represent the following knowledge in semantic network.

Robin is bird

Clyde is a Robin

Clyde owns a nest from spring 2014 to fall 2014 

9.  Represent the following sentences into a semantic network.

                  Birds are animals.

                  Birds have feathers, fly and lay eggs.

                  Albatros is a bird.

                  Donald is a bird.

                  Tracy is an albatross.

8.  What do you mean by membership of an element in a fuzzy set? Given a domain of discourse X={10, 20, 30, 40, 50, 60, 70}, construct a fuzzy set from X. Use your own assumptions for defining membership.

9.  What is Bayes’ rule? Discuss the use of Bayes’ rule for uncertain reasoning.

10.  What is Bayesian Network? Explain how Bayesian Network represents and inference the uncertain knowledge. 

10.  After your yearly checkup, the doctor has bad news and good news. The bad news is that you tested positive for a serious disease, and the test is 99% accurate (i.e. the probability of testing positive given that you have the disease is 0.99, as is the probability of testing negative if you don’t have the disease). The good news is that this is a rare disease, striking only one in 10,000 people.

a. Why is it good news that the disease is rare?

b. What are the chances that you actually have the disease? 

10.  Define the Model-Based and Cased Based system. Discuss which system is suitable for the following problems

i. Electronic circuit testing

ii. Legal Reasoning

10. Convert following statements to FoPL.[6]

No teachers are ignorant.

Some teachers who are ignorant are not skillful.

All skillfull teachers are likely by all.

10.  How facts in uncertain knowledge are represented? Configure a Bayesian network for following:

        The probability of having rain is 60%. The chances of getting cold if it will rain is 80%. The probability of not having sunshine is 90%. The probability that it will be hot if it is sunshine is 0.67.

10.  Convert following sentences to FOPL.

                If every helper is busy then there is a job in the queue.

                A job is in queue but the helper is not busy.

                Every helpers are teased by someone.

10. How uncertain knowledge is represented? Given following full joint probability distribution representing probabilities of having different sizes of CD, find the probability that a CD cover has a length of 130mm given the width is 15mm.

12. What is posterior probability? Consider a scenario that a patient have liver disease is 15% probability. A test says that 5% of patients are alcoholic. Among those patients diagnosed with liver disease, 7% are alcoholic. Now compute the chance of having liver disease, if the patient is alcoholic.

3. Describe mathematical model of neural network. What does it means to train a neural network? Write algorithm for perception learning.

3.  Define mathematical model of artificial neural network. Discuss how Hebbian learning algorithm can be used to train a neural network. Support your answer with an example.

4.  Define learning. Why learning frame work is required? Explain about learning frame work with block diagram and examples.

6.What does it means to train a neural network? Consider following neural network, how back-propagation algorithm can be used to train it?  [6]

6.  Define Learning. Why learning framework is required? Explain about learning frame with block diagram and examples.

6.  Why do we require learning? Explain about learning framework with suitable block diagram and examples.

6.  What is learning by induction? Explain inductive learning process with example.

6.  What is machine learning? How genetic algorithm can be used to train agents? Discuss the operations of genetic algorithm.

7.  Configure a feed-forward neural network with your own assumptions of inputs and weights and express it mathematically. Write an algorithm for training neural networks using allebbian learning.

7.  How concepts of most specific consistent hypothesis and most general consistent hypothesis are used in learning through examples. How generalization specialization tree is maintained for these concepts?

8.  What is a Neural Network? Explain any one type of neural network with practical example.

8.  Derive the mathematical model of neural network. Explain any one type of neural network with its algorithm.

8.  What is neural network? Explain the neural net learning methods.

8.  What is back propagation? Explain all the steps involved in the back propagation with an example.

8.  What do you mean by machine vision? Discuss the components of a machine vision system.

7. What is crossover operation in genetic algorithm? Given following chromosomes show the result of one-point and two point crossover.

        C1 = 01100010

        C2 = 10101100

    Choose appropriate crossover point as per your assumption.

9.  What is machine learning? Explain the learning from analogy and instance based learning?

9.  Write an algorithm for learning by Genetic Approach.

3.  How can you construct expert system? Explain knowledge engineering with a block diagram.

5.  Define a natural language processing. Explain the different issues involved in the natural language processing.

7.  What is an expert system? Explain the architecture and feature of rule-based expert system.

8.  What is natural language processing? How morphological analysis is done during processing?

8. What do you mean by natural language processing? What is the importance of pragmatic analysis in NLP? [3+3]

8. What is expert system? How it works? Mention role of inference engine in expert system.

9.  How can you construct expert system? Explain knowledge engineering with a block diagram.

9.  Knowledge consists of facts, beliefs, and heuristics, justify it. Explain the advantages and disadvantages of an expert system.

9.  Why do we require expert system structure? Draw the block diagram and explain it with practical example.

10.  Differentiate between natural language understanding (NLU) and natural language generating (NLG). Why we have to study natural language processing? Explain it. 

9. How semantic and pragmatic analysis is done in natural language processing.

10.  Define natural language processing. Explain the different issues involved in the natural language processing. 

10.  Explain the steps of Natural Language Processing. 

10.  Explain the different steps involved in the natural language processing (NLP) with block diagram and examples.

10.  Differentiate between natural language understanding (NLU) and natural language generation (NLG).

11.  How the concept of machine vision are used in Robotics to configure sensors of Robots?

12.  How syntactic and semantic analysis is done during natural language processing? Explain with example.