Questions and Answers of Introduction To Artificial Intelligence

Prove the following:a. ⊢ A→Ab. ⊢(( ¬A→¬B)→A)→((¬B→¬A)→¬B)c. ⊢(¬ ¬ ¬A→¬ ¬ ¬B)→(¬A→¬B)
Extend the CLIPS rules given in Section 9.5 to produce a more useful system for running an elevator on a building with five floors. 9.5 CLIPS (C Language Integrated Production System) CLIPS is a
Use the ID3 algorithm to build the full decision tree for the data set given in Section 10.9.2. 10.9.2 Example We will start with the training data given below: Film Country of origin Big star Genre
Implement the nearest neighbor algorithm in the programming language of your choice. The algorithm should work with vectors of up to 10 integer values and allow up to 10 integer classifications.By
Use the operators described in Section 16.2.4 and the STRIPS method to solve the block world planning problem shown in Figure 16.11. The first state shown is the start state and the second state is
Use resolution and unification to solve the block world problem shown in Figure 16.11. How does this plan compare with the one you generated in exercises 16.1and16.2? DATA FROM FIGURE 16.11DATA FROM
Devise your representation for the Missionaries and Cannibals problem and implement it either with pen and paper or in the programming language of your choice. Use it to solve the problem.How
Discuss the current state of the art of game-playing computer systems in relation to the following games: chess, checkers, Go, bridge, Othello, tic-tac-toe. What advances are likely in the near
Discuss the approach you would take to building a system for playing Scrabble or another word game of the sort. What limitations does your system have? How likely do you think it is that your system
What problems did the developers of Chinook face? What new techniques did they add to simple Minimax with alpha-beta pruning? Would these techniques extend well to other games?
Explain why the alpha–beta procedure will always generate the same answer as Minimax without pruning.Why is it useful?
Show the steps that would be taken in running the Minimax algorithm on the game tree in Figure 6.7. Now run through the same tree using alpha-beta pruning. How do the two compare?data from figure 6.7
Why might it be particularly difficult to program a computer to successfully play card games like bridge or poker? What sort of algorithms might you use to play these games?
What does it mean to say that a game has been solved? How likely is it that games like Go and chess will ever be solved? Is it always thecase that the player who goes first will win a game that has
Most commercially available chess programs for home users are designed to play at a range of levels from beginner up to grandmaster. Consider the additional difficulties involved in programming a
For a game tree of depth d, and branching factor b, show that iterative deepening does not increase by a great deal the number of static evaluations needed to examine the tree.
Write an algorithm in pseudo-code, or a programming language of your choice, that evaluates a position in tic-tac-toe. Your static evaluator should give 0 for a drawn position, 1 for a won position
Extend the algorithm you designed for Exercise 6.2 so that it can evaluate positions that are nonterminal—in other words, positions where the game has not yet finished. Your score should be
Implement a Minimax algorithm using your static evaluator for tic-tac-toe, and write a simple program that plays the game. Have the program output how many nodes in the game tree it had to examine as
Add alpha–beta pruning to your program, and see what difference (if any) it makes to the number of nodes the program has to examine when playing a game.
Implement an Expectiminimax algorithm for a game of chance (you might use backgammon, or another dice game).
Is it possible to add alpha–beta pruning to your Expectiminimax program? If so, do so. If not, can you find another way of pruning the tree that improves the performance of the program? How can you
Explain the meanings of the following terms in the context of logic:a. truthb. validityc. equivalentd. uncertaintye. tautologyf. satisfiable g. sound h. complete i. decidable j. modal logic
“Inductive reasoning is a reasonable way to think about everyday life, but it does not provide the logical structure that propositional logic does.”Discuss.
Explain what is meant by the following: “Classical logics are not good at dealing with uncertainty.”
Explain why the addition of the quantifiers ∀ and ∃ makes predicate calculus so powerful.
Explain the rule of modus ponens. Explain how it is used in everyday life.
Explain in layman’s terms what the law of the excluded middle means. What difficulties might you encounter in logical deduction if you ignored the law of the excluded middle?
Assume the law of the excluded middle is not true, and use this to prove the equality 1 = 0.
What does it mean to say that logic is monotonic? Is propositional logic monotonic? What complexities do you think nonmonotonicity would add to the process of logical deduction? Would modus ponens
Translate the following sentences into logical statements, using either propositional or predicate logic as appropriate:a. I like apples and pears.b. When I eat apples and pears, I usually like to
(Hard) Prove that propositional logic and first-order predicate logic are sound and complete.
Write expressions in propositional calculus to represent the following statements:a. If you go to Mexico, you will be far away.b. I cannot hear you when you are far away.c. When I can’t hear you, I
Write expressions in first-order predicate logic to represent the following statements, and prove whether the conclusion follows from the premises or not:a. All dancers love to dance.b. Everyone who
Explain the concept of proof by refutation.
Explain how and to what extent combinatorial problems can be solved using resolution.
Generate the full set of clauses for the map-coloring graph in Figure 8.1. Resolve these clauses to prove that a three-coloring solution does exist for the graph.data from figure 8.1 A B C D
Use resolution to prove that the following logical argument, devised by Lewis Carroll, is valid: No shark ever doubts that it is well-fitted out A fish, that cannot dance a minute, is contemptible No
Explain why expert systems are so called.
Explain the various methods of conflict resolution that can be used in rule-based expert systems. For each of these, give an example of a scenario where using it would not give the correct result.
Describe the architecture of an expert system, and describe the roles of the various people involved in it.
Explain the relationships between rules, logic, semantic nets, and frames. Give an example of a situation where you might use each of them. Which system has the greatest representational
Implement a rule-based or frame-based expert system shell in the programming language of your choice. Implement an expert system in your expert system shell to solve problems in an area in which you
What is meant by inductive bias? Is it a good thing? What is the inductive bias of the ID3 algorithm?
Explain how candidate elimination uses version spaces to learn.
Explain how a system can learn by building decision trees, using the ID3 algorithm.
How does the nearest neighbor algorithm work?
How likely do you think it is that a neural network of the complexity of the human brain will ever be built in software? In hardware?
Explain how Hopfield networks operate.
Explain in detail how a Kohonen map might be used to cluster a set of web documents in response to a user’s keyword query.
“Bayes’ theorem uses a conditional probability and two prior probabilities to calculate just one conditional probability. That doesn’t sound like it’s very helpful.”Discuss this comment.
Explain how Bayes’ theorem can be used to develop learning systems.
Explain how Bayes’ optimal classifier and the naïve Bayes classifier work.
Explain why collaborative filtering is such a useful technique. How successful do you believe it can be? What might limit its efficacy?
Implement a Bayesian belief network in the programming language of your choice to represent a subject in which you are interested (for example, you might use it to diagnose medical conditions from
Implement the naïve Bayes classifier in the programming language of your choice, and use it to classify pages of text, based on which words appear on the page. To do this, you will first need to
Use the following facts to calculate normalized values for P(B|A) and P(¬B|A): P(A) = 0.0025P(B) = 0.015P(A|B) = 0.6P(A|¬B) = 0.25
Examine the collaborative filtering mechanism used by an online shopping system. How effective do you think it is? Might there be more effective methods to achieve the same goal? What kinds of
What is life?
What is Artificial Life? How does it relate to Artificial Intelligence? Is one an alternative to the other, or are they complementary?
Explain how Conway’s Life is modeled on life. What interesting properties does it exhibit? Why do you think it has fascinated people for so long?
Explain how a system might be built that could reproduce itself. Would such a system be alive?
Explain how genetic programming could be used to solve problems.
Explain why L-systems are of interest to Artificial Life researchers.
Explain the relationship between classifier systems and production systems. How are classifier systems built? What advantages do they have over production systems?
Explain how systems modeled on the human immune system might provide a solution to the problem of computer viruses or of unsolicited bulk e-mails (“spam”).
Explain how crossover, mutation, and reproduction affect schemata.
Explain how schemata help us to understand why genetic algorithms work. What does the schema theorem tell us?
Explain why introducing predators can help in systems that use artificial evolutionary techniques.
Describe three problems that might be solved using genetic algorithms that were not described in this chapter.
Could genetic algorithms be used to play complex games such as chess and checkers? Explain your answer.
“Once you’ve chosen a good representation for your problem you might as well solve the problem using traditional means—genetic algorithms are a waste of effort”. Discuss.
What is planning?
Explain why the search methods can be used for planning.
Why can first-order predicate calculus not be used for planning without the addition of situation variables?
Explain the idea behind means–ends analysis. Compare it with search as a planning method. Which more closely matches the methods people use when formulating plans in everyday life?
Write a program in the language of your choice that uses search to formulate plans for moving from one arrangement of three blocks to another. Assume that the available actions that can be taken are
Consider extending your program to work with larger numbers of blocks. How well do you think it will work?
Explain the difference between the STRIPS language and the ADL language. Why is ADL described as being more expressive than STRIPS? What kinds of problems might ADL be used to solve for which STRIPS
Explain how the satisfiability problem relates to planning. How efficient do you think this method might be compared with STRIPS planning or using GraphPlan?
Explain what is meant by dynamic world planning.What is meant by probabilistic planning? What is the difference between probabilistic planning and nondeterministic planning?
What is meant by replanning? b d b a d a C
Explain why case-based planning can be used to produce a planning system that can learn.
Explain why the blackboard architecture is an effective way to combine information from a number of knowledge sources. Describe the main components of the blackboard architecture.
Explain what kinds of problems the Copycat architecture can solve.
Explain what is meant by nonmonotonic reasoning, and explain why it is so important in Artificial Intelligence. Explain the difference between the terms nonmonotonic reasoning and nonmonotonic logic.
Explain what is meant by abductive reasoning. Explain your views on its usefulness in solving the following types of problems:a. solving logical puzzlesb. medical diagnosisc. controlling the behavior
Explain the idea behind temporal logic.What kinds of problems is it useful for solving? Give three examples.
Can semantic networks be used to represent anything that can be represented using temporal logic? Explain your answer.
What is case-based reasoning? From which attributes of human intelligence do you think it is derived? Describe the last time you used case-based reasoning in your normal life.
What is the law of the excluded middle? Argue against the need for this law.
What is a linguistic variable? Give 10 examples of linguistic variables that you might use to describe a building.
What are hedges? Give five hedges that apply to the linguistic variables you gave in answer to question 18.3.DATA IN QUESTION 18.3What is a linguistic variable? Give 10 examples of linguistic
How do fuzzy sets differ from traditional sets? What is the connection between linguistic variables and fuzzy sets?
What advantages would fuzzy expert systems have over traditional expert systems.Would they have any disadvantages?
What is a neuro-fuzzy system? How does it learn? Compare and contrast neuro-fuzzy systems with traditional neural networks.
“A computer virus is a kind of intelligent agent .”Discuss this statement. Consider the various agent properties that have been discussed in this chapter. Which of these properties do computer
Explain the idea behind the BDI architecture. Why do you think this architecture is particularly appealing to human researchers?
Explain the nature of the first six types of Braitenberg vehicles. Discuss how these vehicles can help us to understand the nature of intelligence.
Think of a real-world interface agent. Discuss to what extent this agent has autonomy, learning abilities, and intelligence.

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