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management and artificial intelligence

Questions and Answers of Management And Artificial Intelligence

Exercise 9.8 Explain why we often use discounting of future rewards in MDPs.How would an agent act differently if the discount factor was 0.6 as opposed to 0.9?
Exercise 9.7 Consider the belief network of Exercise 6.8 (page 278). When an alarm is observed, a decision is made whether to shut down the reactor. Shutting down the reactor has a cost cs associated
Exercise 9.6 In Example 9.13 (page 389), suppose that the fire sensor was noisy in that it had a 20% false-positive rate, P(see smoke|report∧¬smoke) = 0.2, and a 15% false negative-rate:P(see
Exercise 9.5 How sensitive are the answers from the decision network of Example 9.13 (page 389) to the probabilities? Test the program with different conditional probabilities and see what effect
Exercise 9.4 Suppose that, in a decision network, there were arcs from random variables “contaminated specimen” and “positive test” to the decision variable“discard sample.” Sally solved
Exercise 9.3 Suppose that, in a decision network, the decision variable Run has parents Look and See. Suppose you are using VE to find an optimal policy and, after eliminating all of the other
Exercise 9.2 Consider the following decision network:This diagram models a decision about whether to cheat at two different time instances.Suppose P(watched) = 0.4, P(trouble1|cheat1, watched) = 0.8,
Exercise 9.1 Students have to make decisions about how much to study for each course. The aim of this question is to investigate how to use decision networks to help them make such decisions.Suppose
Exercise 8.15 The SNLP algorithm is the same as the partial-order planner presented here but, in the protect procedure, the condition is A = A0 and A = A1 and (A deletes G or A achieves G).This
Exercise 8.14 The selection algorithm used in the partial-order planner is not very sophisticated. It may be sensible to order the selected subgoals. For example, in the robot world, the robot should
Exercise 8.13 To implement the function add constraint(A0 < A1, Constraints)used in the partial-order planner, you have to choose a representation for a partial ordering. Implement the following as
Exercise 8.12 Give a condition for the CSP planner that, when arc consistency with search fails at some horizon, implies there can be no solutions for any longer horizon. [Hint: Think about a very
Exercise 8.11 Explain how multiple-path pruning can be incorporated into a regression planner. When can a node be pruned?
Exercise 8.10 For the delivery robot domain, give a heuristic function for the regression planner that is non-zero and an underestimate of the actual path cost. Is it admissible?
Exercise 8.9 Explain how the regression planner can be extended to include maintenance goals, for either the feature-based representation of actions or the STRIPS representation. [Hint: Consider what
Exercise 8.8 In a forward planner, you can represent a state in terms of the sequence of actions that lead to that state.(a) Explain how to check if the precondition of an action is satisfied, given
Exercise 8.7 Suppose you have a STRIPS representation for actions a1 and a2, and you want to define the STRIPS representation for the composite action a1; a2, which means that you do a1 then do
Exercise 8.6 Suggest a good heuristic for a forward planner to use in the robot delivery domain. Implement it. How well does it work?
Exercise 8.5 Suppose we must solve planning problems for cleaning a house.Various rooms can be dusted (making the room dust-free) or swept (making the room have a clean floor), but the robot can only
Exercise 8.3 Write a complete description of the limited robot delivery world, and then draw a state-space representation that includes at least two instances of each of the blocks-world actions
Exercise 8.2 Suppose the robot cannot carry both coffee and mail at the same time. Give two different ways that the CSP that represents the planning problem can be changed to reflect this constraint.
Exercise 8.1 Consider the planning domain in Figure 8.1 (page 350).(a) Give the feature-based representation of the MW and RHM features.(b) Give the STRIPS representations for the pick up mail and
Exercise 7.17 Implement a nearest-neighbor learning system that stores the training examples in a kd-tree and uses the neighbors that differ in the fewest number of features, weighted evenly. How
Exercise 7.16(a) Draw a kd-tree for the data of Figure 7.1 (page 289). The topmost feature to split on should be the one that most divides the examples into two equal classes. Assume that you know
Exercise 7.15 In the neural net learning algorithm, the parameters are updated for each example. To compute the derivative accurately, the parameters should be updated only after all examples have
Exercise 7.14 Run the AIspace.org neural network learner on the data of Figure 7.1 (page 289).(a) Suppose that you decide to use any predicted value from the neural network greater than 0.5 as true,
Exercise 7.13 Give an example where a naive Bayesian classifier can give inconsistent results when using empirical frequencies as probabilities. [Hint: You require two features, say A and B, and a
Exercise 7.12 Show how gradient descent can be used for learning a linear function that minimizes the absolute error. [Hint: Do a case analysis of the error. The error is differentiable at every
Exercise 7.11 As outlined in Example 7.18 (page 322), define a code for describing decision trees. Make sure that each code corresponds to a decision tree (for every sufficiently long sequence of
Exercise 7.10 In choosing which feature to split on in decision-tree search, an alternative heuristic to the max information split of Section 7.3.1 is to use the Gini index.The Gini index of a set of
Exercise 7.9 The decision-tree learning algorithm of Figure 7.5 (page 300) has to stop if it runs out of features and not all examples agree.Suppose that you are building a decision tree and you have
Exercise 7.8 Extend the decision-tree learning algorithm of Figure 7.5 (page 300)so that multivalued features can be represented. Make it so that the rule form of the decision tree is returned.One
Exercise 7.7 The aim of this exercise is to determine the size of the space of decision trees. Suppose there are n binary features in a learning problem. How many different decision trees are there?
Exercise 7.6 Suppose that, in the output of a neural network, we assign any value greater than 0.5 to be true and any less than 0.5 to be false (i.e., any positive value before the activation
Exercise 7.5 Consider Equation (7.1) (page 304), which gives the error of a linear prediction.(a) Give a formula for the weights that minimize the error for the case where n = 1 (i.e., when there is
Exercise 7.4 Consider the decision-tree learning algorithm of Figure 7.5(page 300) and the data of Figure 7.1 (page 289). Suppose, for this question, the stopping criterion is that all of the
Exercise 7.3 Suppose we have a system that observes a person’s TV watching habits in order to recommend other TV shows the person may like. Suppose that we have characterized each show by whether
Exercise 7.2 In the context of a point estimate of a feature with domain {0, 1}with no inputs, it is possible for an agent to make a stochastic prediction with a parameter p ∈ [0, 1] such that the
Exercise 7.1 The aim of this exercise is to fill in the table of Figure 7.3 (page 295).(a) Prove the optimal prediction for training data. To do this, find the minimum value of the absolute error,
Exercise 6.13 Suppose you get a job where the boss is interested in localization of a robot that is carrying a camera around a factory. The boss has heard of variable elimination, rejection sampling,
Exercise 6.12 Consider the problem of generating a dynamic belief network given a particular discretization of time and given a representation in terms of transition time, and the state transition,
Exercise 6.11 Consider the problem of filtering in HMMs (page 271).(a) Give a formula for the probability of some variable Xj given future and past observations. This should involve obtaining a
Exercise 6.10 In importance sampling, every non-observed variable is sampled;a full implementation of VE is not needed. Explain how to compute the probability of a sample given the evidence in this
Exercise 6.9 Let’s continue Exercise 5.14 (page 215).(a) Explain what knowledge (about physics and about students) a beliefnetwork model requires.(b) What is the main advantage of using belief
Exercise 6.8 In a nuclear research submarine, a sensor measures the temperature of the reactor core. An alarm is triggered (A = true) if the sensor reading is abnormally high (S = true), indicating
Exercise 6.7 Explain how to extend VE to allow for more general observations and queries. In particular, answer the following:(a) How can the VE algorithm be extended to allow observations that are
Exercise 6.6 Consider the following belief network:A B CE F Dwith Boolean variables (we write A = true as a and A = false as ¬a) and the following conditional probabilities:P(a) = 0.9 P(b) = 0.2
Exercise 6.5 In this question, you will build a belief network representation of the Deep Space 1 (DS1) spacecraft considered in Exercise 5.10 (page 212). Figure 5.14 (page 213) depicts a part of the
Exercise 6.4 Suppose we want to diagnose the errors school students make when adding multidigit binary numbers. Suppose we are only considering adding two two-digit numbers to form a three-digit
Exercise 6.3 Represent the same scenario as in Exercise 5.8 (page 211) using a belief network. Show the network structure and shade the observed nodes. Give all of the initial factors, making
Exercise 6.2 Consider the belief network of Figure 6.21 (on the next page), which extends the electrical domain to include an overhead projector. Answer the following questions about how knowledge of
Exercise 6.1 Using only the axioms of probability and the definition of conditional independence, prove Proposition 6.5 (page 233).
Exercise 5.13 In this question, consider using integrity constraints and consistency-based diagnosis in a purchasing agent that interacts with various information sources on the web. To answer a
Exercise 5.12 AILog has askables, which are atoms that are asked of the user, and assumables, which are collected in an answer.Imagine you are axiomatizing the wiring in your home and you have an
Exercise 5.11 Consider using abductive diagnosis on the problem in the previous question.Suppose the following:• Valves can be open or closed. For some of them, we know if they are open or closed,
Exercise 5.10 Deep Space One (http://nmp.jpl.nasa.gov/ds1/), a spacecraft launched by NASA in October 1998, used AI technology for its diagnosis and control.For more details, see Muscettola, Nayak,
Exercise 5.9 Consider the following clauses and integrity constraints:false ← a ∧ b.false ← c.a ← d.a ← e.b ← d.b ← g.b ← h.c ← h.Suppose the assumables are {d,e, f , g, h, i}. What
Exercise 5.8 Suppose there are four possible diseases a particular patient may have: p, q, r, and s. p causes spots. q causes spots. Fever could be caused by one (or more) of q, r, or s. The patient
Exercise 5.7 Consider the following knowledge base and assumables aimed to explain why people are acting suspiciously:goto forest ← walking.get gun ← hunting.goto forest ← hunting.get gun ←
Exercise 5.6 This question explores how having an explicit semantics can be used to debug programs. The file elect_bug2.ail in the AILog distribution on the book web site is an axiomatization of the
Exercise 5.5 Abottom-up proof procedure can incorporate an ask-the-user mechanism by asking the user about every askable atom. How can a bottom-up proof procedure still guarantee proof of all
Exercise 5.4 You are given the knowledge base KB containing the following clauses:a ← b ∧ c.b ← d.b ← e.c.d ← h.e.f ← g ∧ b.g ← c ∧ k.j ← a ∧ b.(a) Show how the bottom-up proof
Exercise 5.3 You are given the following knowledge base:a ← b ∧ c.a ← e ∧ f .b ← d.b ← f ∧ h.c ← e.d ← h.e.f ← g.g ← c.(a) Give a model of the knowledge base.(b) Give an
Exercise 5.2 Consider the domain of house plumbing represented in the diagram of Figure 5.13.In this example, p1, p2, and p3 denote cold water pipes; t1, t2, and t3 denote taps; d1, d2, and d3 denote
Exercise 5.1 Suppose we want to be able to reason about an electric kettle plugged into a power outlet for the electrical domain. Suppose a kettle must be plugged into a working power outlet, it must
Exercise 4.12 Pose and solve the crypt-arithmetic problem SEND + MORE =MONEY as a CSP. In a crypt-arithmetic problem, each letter represents a different digit, the leftmost digit cannot be zero
Exercise 4.11 Consider the constraint graph of Figure 4.15 with named binary constraints [e.g., r1 is a relation on A and B, which we can write as r1(A, B)].Consider solving this network using VE.(a)
Exercise 4.10 Explain how VE can be used to count the number of models, without enumerating them all. [Hint: You do not need the backward pass, but instead you can pass forward the number of
Exercise 4.9 Explain how arc consistency with domain splitting can be used to count the number of models.
Exercise 4.8 Explain how VE can be used to return one of the models rather than all of them. Give the algorithm. How is finding one easier than finding all?
Exercise 4.7 Explain how arc consistency with domain splitting can be used to return all of the models and not just one. Give the algorithm.
Exercise 4.6 Which of the following methods can(a) determine that there is no model, if there is not one?(b) find a model if one exists?(c) guarantee to find all models?The methods to consider are i)
Exercise 4.5 Consider a scheduling problem, where there are five activities to be scheduled in four time slots. Suppose we represent the activities by the variables A, B, C, D, and E, where the
Exercise 4.4 Consider how stochastic local search can solve Exercise 4.3. You should use the “stochastic local search” AIspace.org applet to answer this question.Start with the arc-consistent
Exercise 4.3 Consider the crossword puzzle shown in Figure 4.14 . The available words that can be used are at, eta, be, hat, he, her, it, him, on, one, desk, dance, usage, easy, dove, first, else,
Exercise 4.2 Suppose you have a relation v(N,W) that is true if there is a vowel(one of:a, e, i, o, u) as the N-th letter of word W. For example, v(2, cat) is true because there is a vowel (“a”)
Exercise 4.1 Consider the crossword puzzle shown in Figure 4.13. Youmust find six three-letter words: three words read across (A1, A2, and A3) and three words read down (D1, D2, and D3). Each word
Exercise 3.13 The depth-first branch and bound of Figure 3.11 (page 99) is like a depth-bounded search in that it only finds a solution if there is a solution with cost less than bound. Show how this
Exercise 3.12 Give a statement of the optimality of A∗ that specifies the class of algorithms for which A∗ is optimal. Give the formal proof.
Exercise 3.11 Consider the algorithm sketched in the counterexample of the box on page 105:(a) When can the algorithm stop? (Hint: it does not have to wait until the forward search finds a path to a
Exercise 3.10 Bidirectional search must be able to determine when the frontiers intersect. For each of the following pairs of searches specify how to determine when the frontiers intersect:(a)
Exercise 3.9 The overhead for iterative deepening with b−1 on the denominator(page 97) is not a good approximation when b ≈ 1. Give a better estimate of the complexity of iterative deepening when
Exercise 3.8 How can depth-first branch-and-bound be modified to find a path with a cost that is not more than, say 10% greater than the least-cost path. How does this algorithm compare to the
Exercise 3.7 Suppose that, rather than finding an optimal path from the start to a goal, we wanted a path with a cost not more than, say, 10% greater than the leastcost path. Suggest an alternative
Exercise 3.6 Implement iterative-deepening A∗. This should be based on the iterative deepening searcher of Figure 3.10 (page 97).
Exercise 3.5 Draw two different graphs, indicating start and goal nodes, for which forward search is better in one and backward search is better in the other.
Exercise 3.4 This question investigates using graph searching to design video presentations. Suppose there exists a database of video segments, together with their length in seconds and the topics
Exercise 3.3 Consider the problem of finding a path in the grid shown in Figure 3.13 from the position s to the position g. A piece can move on the grid horizontally and vertically, one square at a
Exercise 3.2 Which of the path-finding search procedures are fair in the sense that any element on the frontier will eventually be chosen? Consider this for question finite graphs without loops,
Exercise 3.1 Comment on the following quote: “One of the main goals of AI should be to build general heuristics that can be used for any graph-searching problem.”
Exercise 2.9 Suppose you have a new job and must build a controller for an intelligent robot. You tell your bosses that you just have to implement a command function and a state transition function.
Exercise 2.8 Change the controller so that the robot senses the environment to determine the coordinates of a location. Assume that the body can provide the coordinates of a named location.
Exercise 2.7 The current controller visits the locations in the todo list sequentially.(a) Change the controller so that it is opportunistic; when it selects the next location to visit, it selects
Exercise 2.6 When the user selects and moves the current target location, the robot described in this chapter travels to the original position of that target and does not try to go to the new
Exercise 2.5 Consider the “robot trap” in Figure 2.11.(a) Explain why it is so tricky for a robot to get to location g. You must explain what the current robot does as well as why it is difficult
Example 2.5 (page 55) would not be able to get around (and it will crash or loop).(b) Even without obstacles, the robot may never reach its destination. For example, if it is next to its target
Exercise 2.4 The obstacle avoidance implemented in Example 2.5 (page 55) can easily get stuck.(a) Show an obstacle and a target for which the robot using the controller of
Exercise 2.3 The definition of the target position in Example 2.6 (page 56) means that, when the plan ends, the robot will just keep the last target position as its target position and keep circling
Exercise 2.2 Explain why the middle layer in Example 2.5 (page 55) must have both the previous target position and the current target position as inputs. Suppose it had only one of these as input;
Exercise 2.1 Section 2.3 (page 50) argued that it was impossible to build a representation of a world that is independent of what the agent will do with it. This exercise lets you evaluate this

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