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management and artificial intelligence

Questions and Answers of Management And Artificial Intelligence

2.7 Implement an environment for anxm rectangular room, where each square has a 5% chance of containing dirt, and n and m are chosen at random from the range 8 to 15, inclusive.
2.6 Implement a table-lookup agent for the special case of the vacuum-cleaner world consisting of a 2 x 2 grid of open squares, in which at most two squares will contain dirt. The agent starts in the
2.5 Implement a performance-measuring environment simulator for the vacuum-cleaner world.This world can be described as follows:
2.4 While driving, which is the best policy?a. Always put your directional blinker on before turning,b. Never use your blinker,c. Look in your mirrors and use your blinker only if you observe a car
2.3 Choose a domain that you are familiar with, and write a PAGE description of an agent for the environment. Characterize the environment as being accessible, deterministic, episodic, static, and
2.2 For each of the environments in Figure 2.3, determine what type of agent architecture is most appropriate (table lookup, simple reflex, goal-based or utility-based).
2.1 What is the difference between a performance measure and a utility function?
1.10 "Surely animals cannot be intelligent—they can only do what their genes tell them." Is the latter statement true, and does it imply the former?
1.9 "Surely computers cannot be intelligent—they can only do what their programmers tell them." Is the latter statement true, and does it imply the former?
1.8 Some authors have claimed that perception and motor skills are the most important part of intelligence, and that "higher-level" capacities are necessarily parasitic—simple add-ons to these
1.7 Fact, fiction, and forecast:a. Find a claim in print by a reputable philosopher or scientist to the effect that a certain capacity will never be exhibited by computers, where that capacity has
1.6 Find an article written by a lay person in a reputable newspaper or magazine claiming the achievement of some intelligent capacity by a machine, where the claim is either wildly exaggerated or
1.5 Examine the AI literature to discover whether or not the following tasks can currently be solved by computers:a. Playing a decent game of table tennis (ping-pong).b. Driving in the center of
1.4 Suppose we extend Evans's ANALOGY program so that it can score 200 on a standard IQ test. Would we then have a program more intelligent than a human? Explain.
1.3 There are well-known classes of problems that are intractably difficult for computers, and other classes that are provably undecidable by any computer. Does this mean that AI is impossible?
1.2 We characterized the definitions of AI along two dimensions, human vs. ideal and thought vs. action. But there are other dimensions that are worth considering. One dimension is whether we are
1.1 Read Turing's original paper on AI (Turing, 1950). In the paper, he discusses several potential objections to his proposed enterprise and his test for intelligence. Which objections still carry
Implement a version of the chart-parsing algorithm that returns a packed tree of all edges that span the entire input.
Consider the sentence "Someone walked slowly to the supermarket" and the following lexicon:Pronoun -+ someone V -+ walked Adv -+ slowly Prep + to Det -+ the Noun -t supermarket Which of the following
Without looking back at Exercise 22.1, answer the following questioAs:a. What are the four steps that are mentioned?b. What step is left out?c. What is "the material" that is mentioned in the text?d.
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.Would it be a good idea
Augment the El grammar so that it handles article-noun agreement. That is, make sure that "agents" is an NP, but "agent" and "an agents7' are not.
Implement the REINFORCE and PEGASUSa lgorithms and apply them to the 4 x 3 world, using a policy family of your own choosing. Comment on the results.
Extend the standard game-playing environment (Chapter 6) to incorporate a reward signal. Put two reinforcement learning agents into th~e environment (they may, of course, share the agent program) and
Compute the true utility function and the best linear approximation in x and y (as in Equation (21.9)) for the following environments:a. A 10 x 10 world with a single $1 terminal state at (10,lO).b.
Write out the parameter update equations for TI) learning with - (x,y) =00+01+02y+03 (xg)2 + (y-g)2
Implement an exploring reinforcement learning agent that uses direct utility estimation.Make two versions-ne with a tabular representation and one using the function approximator in Equation (21.9).
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 providing
Chapter 17 defined a proper policy for an MDP as one that is guaranteed to reach a terminal state. Show that it is possible for a passive ADP agent to learn a transition model for which its policy n
Consider the problem of separating N data points into positive and negative examples using a linear separator. Clearly, this can always be done for N = 2 points on a line of dimension d = 1,
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 backpropagation network
Starting from Equation (20.13), show that dL/aWj = Err x xj.
Consider the following set of examples, each with six inputs and one target output:a. Run the perceptron learning rule on these data arid show the final weights.b. Run the decision tree learning
Recall from Chapter 18 that there are 22n distinct Boolean functions of n inputs. How many of these are representable by a threshold perceptron?
Construct a support vector machine that computes the XOR function. It will be convenient to use values of 1 and -1 instead of 1 and 0 fair the inputs and for the outputs. So an example looks like (
Consider the application of EM to learn the parameters for the network in Figure 20.1 O(a), given the true parameters in Equation (20.7).a. Explain why the EM algorithm would not work if there were
This exercise investigates properties of the Beta distribution defined in Equation (20.6).a. By integrating over the range [0, 11, show that the normalization constant for the distribution beta[a, b]
Consider the noisy-OR model for fever described in Section 14.3. Explain how to apply maximum-likelihood learning to fit the parameters of such a model to a set of complete data. (Hint: use the chain
Consider m data points ( x j, yj ) , where the yj s are generated from the xj s according to the linear Gaussian model in Equation (20.5). Find the values of 01, 02, and CT that maximize the
Explain how to apply the boosting method of Chapter 18 to naive Bayes learning. Test the performance of the resulting algorithm on the restaurant learning problem.
Suppose that Ann's utilities for cherry and lime candies are c~ and QA, whereas Bob's utilities are cg and QB. (But once Ann has unwrappt:d a piece of candy, Bob won't buy it.) Presumably, if Bob
Repeat Exercise 20.1, this time plotting the values of P(Dm+l = lime1 hnlAp) and P(Dmtl = limeIhM~).
The data used for Figure 20.1 can be viewed as being generated by h5. For each of the other four hypotheses, generate a data set of length 100 and plot the corresponding graphs for P(hi(dl,.. . ,dm)
Using the data from the family tree in Figure 19.11, or a subset thereof, apply the FOIL algorithm to learn a definition for the Ancestor predicate.
Suppose one writes a logic program that carries out a resolution inference step. That is, let Resolve(cl, c2,c) succeed if c is the result of resolving cl and cz. Normally, Resolve would be used as
Fill in the missing values for the clauses C1 or Cz (or both) in the following sets of clauses, given that C is the resolvent of Cl and Cz:a. C = True + P(AB,) ,C 1 = P(x,y)+ Q(xy,), Cz =??.b. C =
Show, by translating into conjunctive normal form and applying resolution, that the conclusion drawn on page 694 concerning Brazilians is sound.
Consider an ensemble learning algorithm that uses simple majority voting among M learned hypotheses. Suppose that each hypothesis has error E and that the errors made by each hypothesis are
Modify DECISION-TREE-LEARNING to include X2-pruning. You might wish to consult Quinlan (1986) for details.
In the recursive construction of decision trees, it sometimes happens that a mixed set of positive and negative examples remains at a leaf node, even after all the attributes have been used. Suppose
Solve the game of three-finger Mona.
Show that a dominant strategy equilibrium is a Nash equilibrium, but not vice versa.
on page 190. Draw the state space (rather than the game tree), showing the moves by A as solid lines and moves by B as dashed lines. Mark each state with R(s). You will find it helpful to arrange the
In this exercise we will consider two-player MDPs that correspond to zero-sum, turntaking games like those in Chapter 6. Let the players be: A and B, and let R(s) be the reward for player A in s.
For the environment shown in Figure 17.1, find all the threshold values for R(s) such that the optimal policy changes when the threshold is crossed. You will need a way to calculate the optimal
For the 4 x 3 world shown in Figure 17.1, calculate which squares can be reached from(1,l) by the action sequence [Up, Up, Right, Right, Right] and with what probabilities. Explain how this
Modify and extend the Bayesian network code in the code repository to provide for creation and evaluation of decision networks and the calculation of information value.The answers to Exercise
(Adapted from Pearl (1988).) A used-car buyer can decide to carry out various tests with various costs (e.g., kick the tires, take the car to a qualified mechanic) and then, depending on the outcome
and 16.9, to which conditional probability table entry is the utility most sensitive, given the available evidence?
For either of the airport-siting diagrams from Exercises
Repeat Exercise 16.8, using the action-utility representation shown in Figure 16.6.
Show that if X1 and X2 are preferentially independent of X3, and X2 and X3 are preferentially independent of X1, then X3 and XI are prefere:ntially independent of X2.
Assess your own utility for different incremental amounts of money by running a series of preference tests between some definite amount MI and a lottery Ip, M2; (1 -p), 01. Choose different values of
Tickets to a 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 of a lottery
Calculate the most probable path through the HMM. in Figure 15.20 for the output sequence [C1, C2, C3, C4, C4, C6, C7]. Also give its probability.
Consider applying the variable elimination algorithm to the umbrella DBN unrolled for three slices, where the query is P(R31Ul, U2, Us). Shtow that the complexity of the algorithm--the size of the
In this exercise, we analyze in more detail the persistent-failure model for the battery sensor in Figure 15.13(a).a. Figure 15.13(b) stops at t = 32. Describe qualitatively what should happen as t
Let us examine the behavior of the variance update in Equation (15.18).a. Plot the value of a? as a function oft, given various values for a; and 02.b. Show that the update has a fixed point a2 such
On page 547, we outlined a flawed procedure for finding the most likely state sequence, given an observation sequence. The procedure involves finding the most likely state at each time step, using
This exercise develops a space-efficient variant of the forward-backward algorithm described in Figure 15.4. We wish to compute P(Xklel,t) for lc = 1, . . . , t. This will be done with a
In this exercise, we examine what happens to the probabilities in the umbrella world in the limit of long time sequences.a. Suppose we observe an unending sequence of days on which the umbrella
Three soccer teams A, B, and C, play each other once. Each match is between two teams, and can be won, drawn, or lost. Each team has a fixed, unknown degree of qualityan integer ranging from 0 to
1(a)and how MCMC can answer it.a. How many states does the Markov chain have?b. Calculate the transition matrix Q containing q(y + y') for all y, y'.c. What does Q~th,e square of the transition
Consider the query P(Rain(Sprink1er= true, WetGrass= true) in Figure
The Markov blanket of a variable is defined on page 499.a. Prove that a variable is independent of all other variables in the network, given its Markov blanket.b. Derive Equation (14.11).
Consider the network shown in Figure 14.19(ii), and assume that the two telescopes work identically. N E {1,2,3) and MI, M2 E {Oil, 2,3,4), with the symbolic CPTs as described in Exercise 14.3. Using
In our analysis of the wumpus world, we used the fact that each square contains a pit with probability 0.2, independently of the contents of the other squares. Suppose instead that exactly N/5 pits
(Adapted from Pearl (1988).) Suppose you are a witness to a nighttime 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
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 P(hlel, ez)
In this exercise, you will complete the normalization calculation for the meningitis example. First, make up a suitable value for P(S(lM)a,n d use it to calculate unnormalized values for P(MIS) and
Show that the statementis equivalent to either of the statements P(AIB,C) =P(A(C) and P(BIA,C)=P(B(C). P(A, B|C) P(A|C)P(B|C)
Give11 the full joint distribution shown in Figure 13.3, calculate the following:a. P(toothache)b. P(Cavity)c. P( Toothache (cavity)d. P(Cavity(toothache V catch).
This question deals with the properties of atomic events, as discussed on page 468.a. Prove that the disjunction of all possible atomic events is logically equivalent to true.[Hint: Use a proof by
Would it be rational for an agent to hold the three beliefs P(A) = 0.4, 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 B? Make up a
Show from first principles that P(alb Aa) = 1.
To the medication problem in the previous exercise, add a Test action that has the conditional effect CultureGrowth when Disease is true and in any case has the perceptual effect Known ( Culture
Look at the list on page 445 of things that the replanning agent can't do. Sketch an algorithm that can handle one or more of them.
Explain precisely how to modify the AND-OR-GRAPH-SEARCH algorithm to generate a cyclic plan if no acyclic plan exists. You will need to deal with three issues: labeling the plan steps so that a
for an example.) Determine the information that should be stored and how the algorithm should use that information when a repeated state is found. (Hint: You will need to distinguish at least between
checks for repeated states only on the path from the root to the current state. Suppose that, in addition, the algorithm were to store every visited state and check against that list. (See
The AND-OR-GRAPH-SEARCH algorithm in Figure
Show how a standard STRIPS action description can be rewritten as an HTN decomposition, using the notation Achieve(p) to denote the actilvity of' achieving the condition p.
Give an example in the house-building domain of two1 abstract subplans that cannot be merged into a consistent plan without sharing steps. (.Hint: Places where two physical parts of the house come
Give decompositions for the HireBuilder and GetPernzit steps in Figure 12.7, and show how the decomposed subplans connect into the overalll plan.
so that there are KO0 screws initially, engine El requires 40 screws, and engine E:2 requires 50 screws. 'The + and- function symbols may be used in effect literals for resources.b. Explain how the
A consumable resource is a resource that is (partially) used up by an action. For example, attaching engines to cars requires screws. The screws, once used, are not available for other attachments.a.
Examine carefully the representation of time aind resources in Section 12.1.a. Why is it a good idea to have Duration(d) be a11 effect of an action, rather than having a separate field in the action
In the SATPLAN algorithm in Figure 11.15, each call to the satisfiability algorithm asserts a goal gT, where T ranges from 0 to T,,,. Suppose instead that the satisfiability algorithm is called only
Giving examples from the airport domain, explain how symbol-splitting reduces the size of the precondition axioms and the action exclusion axioms. Derive a general formula for the size of each axiom
Up to now we have assumed that actions are only executed in the appropriate situations.Let us see what propositional successor-state axioms sixh as Equation (1 1.1) have to say about actions whose

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