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management and artificial intelligence

Questions and Answers of Management And Artificial Intelligence

24.1 In the shadow of a tree with a dense, leafy canopy, one sees a number of light spots.Surprisingly, they all appear to be circular. Why? After all, the gaps between the leaves through which the
23.14 Implement a version of the chart-parsing algorithm that returns a packed tree for the longest leftmost edge, and then if that edge does not span the whole input, continues the parse from the
23.13 Implement a version of the chart-parsing algorithm that returns a packed tree of all edges that span the entire input.
23.12 We forgot to mention that the title of the text in Exercise 23.1 is "Washing Clothes." Go back and reread the text, and answer the questions in Exercise 23.3. Did you do better this
23.11 One way to define the task of spelling correction is this: given a misspelled word and a dictionary of correctly spelled words, find the word(s) in the dictionary that can be transformed into
23.10 Write grammar rules for the category Adjp, or adjective phrase, using reified categories.Show how to derive 3 g (g £ Fake(Guns) as the semantics of "a fake gun." An adjective phrase can be
23.9 We said that 3e, x e E Sleep(John, Past) A Name(x) = John was a plausible interpretation for "John slept." But it is not quite right, because it blurs the distinction between "John slept"and
23.7 Collect some examples of time expressions, such as "two o'clock," "midnight," and"12:46." Also think up some examples that are ungrammatical, such as "thirteen o'clock" or"half past two
23.5 Open any book or magazine to a random page and copy down the first 10 sentences. How many of them are in £3 ? Show the parses of the sentences that are, and explain what went wrong for the
23.4 Open any book or magazine to a random page and write down the first 20 nominal compounds you find. Characterize the semantic relations (e.g., made-of, used-for, etc.).
23.3 Without looking back at Exercise 23.1, answer the following questions:• What are the four steps that are mentioned?• What step is left out?• What is "the material" that is mentioned in the
23.2 Describe how a simple pseudo-natural-language (template-based) explanation facility can be built for a vanilla, backward-chaining, logical reasoning system. The explanation facility should be
22.5 This exercise concerns grammars for very simple languages.a. Write a context-free grammar for the language a"b".b. Write a context-free grammar for the palindrome language: the set of all
22.4 Augment the grammar from this chapter so that it handles the following:a. Pronoun case.b. Subject/verb agreement.c. Article/noun agreement: "agents" is an NP but "agent" is not. In general, only
22.3 Determine what semantic interpretation would be given to the following sentences by the grammar in this chapter:a. It is a wumpus.b. The wumpus is dead.c. The wumpus is in 2,2.
22.1 Outline the major differences between Pascal (or any other computer language with which you are familiar) and English, and the "understanding" problem in each case. Think about such things as
21.7 Using the data from the family tree in Figure 21.5, or a subset thereof, apply the FOIL algorithm to learn a definition for the Ancestor predicate.
21.4 Fill in the missing values for the clauses C\ and/or €2 in the following sets of clauses, given that C is the resolvent of C\ and €2-a. C= True => P(A,B}, C\ = P(x,y) =^ Q(x,y), C2 =??.b. C
21.3 Would a probabilistic version of determinations be useful? Suggest a definition.
21.1 Show, by translating into conjunctive normal form and applying resolution, that the conclusion drawn on page 633 concerning Brazilians is sound.
20.11 (Discussion topic.) Is reinforcement learning an appropriate abstract model for human learning? For evolution?
20.10 Extend the standard game-playing environment (Chapter 5) to incorporate a reward signal. Put two reinforcement learning agents into the environment (they may of course share the agent program)
20.9 Write down the update equation for Q-learning with a parameterized implicit representation.That is, write the counterpart to Equation (20.8).
20.8 Adapt the vacuum world (Chapter 2) for reinforcement learning by including rewards for picking up each piece of dirt and for getting home and switching off. Make the world accessible by
20.7 How can the value determination algorithm be used to calculate the expected loss experienced by an agent using a given set of utility estimates U and an estimated model M, compared to an agent
20.6 Prove formally that Equations (20.1) and (20.3) are consistent with the definition of utility as the expected reward-to-go of a state.
20.5 The description of reinforcement learning agents in Section 20.1 uses distinguished terminal states to indicate the end of a training sequence. Explain how this additional complication could be
20.4 The environments used in the chapter all assume that training sequences are finite. In environments with no clear termination point, the unlimited accumulation of rewards can lead to problems
20.2 Implement a passive learning agent in a simple environment, such as that shown in Figure 20.1. For the case of an initially unknown environment model, compare the learning performance of the
20.1 Show that the estimates developed by the LMS-UPDATE algorithm do indeed minimize the mean square error on the training data.
19.7 The network in Figure 19.13 has four hidden nodes. This number was chosen somewhat arbitrarily. Run systematic experiments to measure the learning curves for networks with different numbers of
19.6 Suppose that a training set contains only a single example, repeated 100 times. In 80 of the 100 cases, the single output value is 1; in the other 20, it is 0. What will a back-propagation
19.5 Implement a data structure for layered, feed-forward neural networks, remembering to provide the information needed for both forward evaluation and backward propagation. Using this data
19.4 Considerthe following set of examples. Each example has six inputs and one target output:/Ih hh Is/6 T1 1 0 0 1 1 0 1 0 0 0 0 1 1 1010101 11 0011 11 11100 11 1 0 0 0 0 0 0 1 1 1 0 1 0 011 10 11
19.2 We know that a simple perceptron cannot represent XOR (or, generally, the parity function of its inputs). Describe what happens to the weights of a four-input, step-function perceptron,
19.1 Construct by hand a neural network that computes the XOR function of two inputs. Make sure to specify what sort of units you are using.
18.14 We have shown how a learning element can improve the performance element. What if we wanted to improve the learning element (or the critic or the problem generator)? Give some examples of this
18.13 In this ^exercise, we will consider the expressiveness of decision lists, as defined in Section 18.6.a. Show that if the tests can be of any size, decision lists can represent any Boolean
18.10 Modify DECISION-TREE-LEARNING to include x2-pruning. You may wish to consult Quinlan (1986) for details.
18.9 Suppose that an attribute splits the set of examples E into subsets £,, and that each subset has pi positive examples and «, negative examples. Show that unless the ratio />//(/?,• + «,) is
18.8 Suppose that a learning algorithm is trying to find a consistent hypothesis when the classifications of examples are actually being generated randomly. There are n Boolean attributes, and
18.7 In the recursive construction of decision trees, it sometimes occurs that a mixed set of positive and negative examples remains at a leaf node, even after all the attributes have been
18.6 Look back at Exercise 3.16, which asked you to predict from a sequence of numbers(such as [1,4,9,16]) the function underlying the sequence. What techniques from this chapter are applicable to
18.5 A good "straw man" learning algorithm is as follows: create a table out of all the training examples. Determine which output occurs most often among the training examples; call it d.Then when
18.4 We never test the same attribute twice along one path in a decision tree. Why not?
18.3 Draw a decision tree for the problem of deciding whether or not to move forward at a road intersection given that the light has just turned green.
18.2 Repeat Exercise 18.1 for the case of learning to play tennis (or some other competitive sport with which you are familiar). Is this supervised learning or reinforcement learning?
18.1 Consider the problem faced by an infant learning to speak and understand a language.Explain how this process fits into the general learning model, identifying each of the components of the model
17.5 Prove that the calculations in the prediction and estimation phases of the basic decision cycle (Equations (17.8) and (17.9)) do in fact yield the correct value for5e/(X,), given
17.4 For the environment shown in Figure 17.1, find all the threshold values for the cost of a step, such that the optimal policy changes when the threshold is crossed.
17.2 For a specific environment (which you can make up), construct a utility function on histories that is not separable. Explain how the concept of utility on states fails in this case.
17.1 For the stochastic version of the world shown in Figure 17.1, calculate which squares can be reached by the action sequence [Up,Right], and with what probabilities.
16.12 How much is a micromort worth to you? Devise a protocol to determine this.
16.11 Prove that the value of information is nonnegative, as stated in Section 16.6.
16.9 For either of the airport-siting diagrams constructed in Exercises 16.7 and 16.8, to which conditional probability table entry is the utility most sensitive, given the available evidence?
16.8 Repeat Exercise 16.7, using the action-utility representation shown in Figure 16.5.
16.7 Encode the airport-siting problem as shown in Figure 16.4, provide reasonable probabilities and utilities, and solve the problem for the case of choosing among three sites. What happens if
16.6 Show that if X{ and X2 are preferentially independent of X3, and X2 and X3 are preferentially independent of X|, then it follows that X3 and Xi are preferentially independent of X2.
16.5 It has sometimes been suggested that lexicographic preference is a form of rational behavior that is not captured by utility theory. Lexicographic preferences rank attributes in some order X i ,
16.4 Write a computer program to automate the process in Exercise 16.3. Try your program out on several people of different net worth and political outlook. Comment on the consistency of your
16.3 Assess your own utility for different incremental amounts of money. Do this by running a series of preference tests between some definite amount M\ and a lottery [p,M2', (1 — p), 0].Choose
16.2 Tickets to the state lottery cost $1. There are two possible prizes: a $10 payoff with probability 1/50, and a $1,000,000 payoff with probability 1/2,000,000. What is the expected monetary value
15.6 Is probabilistic reasoning monotonic or nonmonotonic? Do these concepts even apply to probabilities?
14.15 Prove that the three axioms of probability are necessary for rational behavior in betting situations, as shown by de Finetti
14.14 In previous chapters, we found the technique of reiflcation useful in creating representations in first-order logic. For example, we handled change by reifying situations, and belief by
14.13 This exercise concerns Bayesian updating in the meningitis example. Starting with a patient about whom we know nothing, show how the probability of having meningitis, P(M), is updated after we
14.12 (Adapted from Pearl (1988).) Three prisoners, A, B, and C, are locked in their cells. It is common knowledge that one of them will be executed the next day and the others pardoned.Only the
14.11 (Adapted from Pearl (1988).) You are a witness of a night-time hit-and-run accident involving a taxi in Athens. All taxis in Athens are blue or green. You swear, under oath, that the taxi was
14.10 Express the statement that X and Y are conditionally independent given Z as a constraint on the joint distribution entries for P(X, Y, Z).
14.9 This exercise investigates the way in which conditional independence relationships affect the amount of information needed for probabilistic calculations.a. Suppose we wish to calculate
14.8 Show that the degree of belief after applying the Bayesian updating process is independent of the order in which the pieces of evidence arrive. That is, show that P(A\B, C) = P(A\C,B)using the
14.7 In this exercise, you will complete the normalization calculation for the meningitis example.First, make up a suitable value for P(S|->A/), and use it to calculate unnormalized values for P(M\S)
14.6 Show that the statement P(A,B\C) = P(A\C)P(B\C)is equivalent to the statement P(A|fi,C) = P(A|C)and also to P(fi|A,C) = P(fi|C)
14.5 It is quite often useful to consider the effect of some specific propositions in the context of some general background evidence that remains fixed, rather than in the complete absence of
14.4 Would it be rational for an agent to hold the three beliefs P(A) = OA, P(B) = 0.3, and P(A V B) = 0.5? If so, what range of probabilities would be rational for the agent to hold for A A Bl Make
14.3 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 that the test is 99% accurate (i.e., the probability of
14.2 Consider the domain of dealing five-card poker hands from a standard deck of 52 cards, under the assumption that the dealer is fair.a. How many atomic events are there in the joint probability
14.1 Show from first principles that P(A|BAA)= 1
13.6 Softbots construct and execute plans in software environments. One typical task for softbots is to find copies of technical reports that have been published at some other institution.Suppose
13.5 In this exercise, we will add nondeterminism to the environment from Exercise 13.4.a. Modify your environment so that with probability 0.1, an action fails—that is, one of the effects does not
13.4 This exercise involves the use of POP to actually fix a flat tire (in simulation).a. Build an environment simulator for the flat-tire world. Your simulator should be able to update the state of
13.3 Represent the actions for the flat-tire domain in the appropriate format, formulate the initial and goal state descriptions, and use the POP algorithm to solve the problem.
13.2 Discuss the application of conditional planning and replanning techniques to the vacuum world and wumpus world.
13.1 Consider how one might use a planning system to play chess.a. Write action schemata for legal moves. Make sure to include in the state description some way to indicate whose move it is. Will
12.10 Some of the operations in standard programming languages can be modelled as actions that change the state of the world. For example, the assignment operation changes the contents of a memory
12.9 Some domains have resources that are monotonically decreasing or increasing. For example, time is monotonically increasing, and if there is a Buy operator, but no Earn, Beg, Borrow, or Steal,
12.8 We said in Section 11.6 that the SELECT-SUB-GOAL part of the POP algorithm was not a backtrack point—that we can work on subgoals in any order without affecting completeness (although the
12.7 Write operators for the shopping domain that will enable the planner to achieve the goal of having three oranges by grabbing a bag, going to the store, grabbing the oranges, paying for them, and
12.6 Add existential quantifiers (3) to the plan language, using whatever syntax restrictions you find reasonable, and extend the planner to accommodate them.
12.5 Prove that the upward solution property always holds for approximation hierarchy planning(see page 380). You may use Tenenberg (1988) for hints.
12.4 Construct an example of the violation of the downward solution property. That is, find an abstract solution such that, when one of the steps is decomposed, the plan becomes inconsistent in that
12.3 Give an example in the house-building domain of two abstract subplans that cannot be merged into a consistent plan without sharing steps. (Hint: Places where two physical parts of the house come
12.2 Rework the previous exercise using an approximation hierarchy. That is, assign criticality levels to each precondition of each step. How did you decide which preconditions get higher criticality
12.1 Give decompositions for the Hire Builder and Obtain Permit steps in Figure 12.1, and show how the decomposed subplans connect into the overall plan.
11.9 In this exercise we will consider the monkey-and-bananas problem, in which there is a monkey in a room with some bananas hanging out of reach from the ceiling, but a box is available that will
11.8 POP is a nondeterministic algorithm, and has a choice about which operator to add to the plan at each step and how to resolve each threat. Can you think of any domain-independent heuristics for
11.7 In this exercise, we will look at planning in Shakey's world.a. Describe Shakey's six actions in situation calculus notation.b. Translate them into the STRIPS language.c. Either manually or
11.6 The POP algorithm shown in the text is a regression planner, because it adds steps whose effects satisfy unsatisfied conditions in the plan. Progression planners add steps whose preconditions
11.4 Figure 11.16 shows a blocks-world planning problem known as the Sussman anomaly.The problem was considered anomalous because the noninterleaved planners of the early 1970s could not solve it.
11.3 There are many ways to characterize planners. For each of the following dichotomies, explain what they mean, and how the choice between them affects the efficiency and completeness of a

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