Artificial Intelligence Programming With Python: From Zero To Hero 1st Edition Perry Xiao - Solutions

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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? How many different functions are represented by these decision trees? Is it possible that two
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 function is true, and a negative value is false).Run the AIspace.org neural network learning applet on the
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 only one input feature). [Hint: For each weight, differentiate with respect to that weight and set
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 examples have the same classification. The tree of Figure 7.4 (page 298) was built by selecting a feature
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 it is a comedy, whether it features doctors, whether it features lawyers, and whether it has guns.
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 agent predicts 1 with probability p and predicts 0 otherwise. For each of the following error
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, the sum-of-squares error, the entropy, and the value that gives the maximum likelihood. The maximum or
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, and particle filtering and wants to know which would be most suitable for this task. You must write
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, as in Section 6.5.5 (page 274). Suppose that there is an exponential distribution of how long each
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 factor from the previous state and a factor from the next state and combining them to determine the
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 situation. [Hint: remember that it is possible to sample children as well as parents of observed
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 networks (over using abductive diagnosis or consistency-based diagnosis) in this domain?(c) What is the
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 an overheating of the core (C = true). The alarm and/or the sensor can be defective (S ok = false, A
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 disjunctions of values for a variable (e.g., of the form X = a ∨ X = b)?(b) How can the VE
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 P(c|a,b) = 0.1 P(c|a, ¬b) = 0.8 P(c|¬a,b) = 0.7 P(c|¬a, ¬b) = 0.4 P(d|b) = 0.1 P(d|¬b) = 0.8
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 actual DS1 engine design.Suppose the following scenario:• Valves can be open or closed.• A
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 number.That is, the problem is of the form:A1 A0 + B1 B0 C2 C1 C0 , where Ai, Bi, and Ci are all binary
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 reasonable assumptions about the conditional probabilities(they should follow the story given in that
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 the values of some variables would affect the probability of another variable:(a) Can 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 question, the purchasing agent will ask a number of the information sources for facts. However,
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 axiomatization similar to that of Example 5.3 (page 162). You are axiomatizing the domain for a new
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, and for some valves we do not know.• A valve can be ok, in which case the gas will flow if the
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, Pell, and Williams [1998] or http://ic.arc.nasa.gov/projects/remote-agent/ (although these references
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 are the minimal conflicts?
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 has spots and fever. Suppose you have decided to use abduction to diagnose this patient based on the
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 ← robbing.goto bank ← robbing.goto bank ← banking.fill withdrawal form ← banking.false ←
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 electrical wiring domain of Figure 5.2 (page 164), but it contains a buggy clause (one that is false
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 (non-askable) atoms that are a logical consequence of a definite-clause knowledge base without asking the
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 procedure works for this example. Give all logical consequences of KB.(b) f is not a logical
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 interpretation that is not a model of the knowledge base.(c) Give two atoms that are logical consequences
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 drainage pipes; shower denotes a shower; bath denotes a bath; sink denotes a sink; and floor
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 be turned on, and it must be filled with water, in order to heat.Using AILog syntax, write axioms
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 (because then it would not be there), and the sum must be correct considering each sequence of letters as
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) Suppose you were to eliminate variable A. Which constraints are removed?A constraint is created on
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 solutions there would be.]
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) arc consistency with domain splitting.ii) variable elimination.iii) stochastic local search.iv)
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 domain of each variable is {1, 2, 3, 4} and the constraints are A > D, D > E, C = A, C > E, C = D, B
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 network.(a) How well does random walking work?(b) How well does hill climbing work?(c) How well does
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, loses, fuels, help, haste, given, kind, sense, soon, sound, this, think.(a) Given the representation
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”) as the second letter of the word “cat”;v(3, cat) is false, because the third letter of “cat”
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 must be chosen from the list of forty possible words shown. Try to solve it yourself, first by
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 can be combined with an iterative deepening search to increase the depth bound if there is no
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 goal).(b) What data structures should be kept?(c) Specify the algorithm in full.(d) Show that it
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) Breadth-first search and depth-bounded depth-first search.(b) Iterative deepening search and depth-bounded
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 b = 1. What is the complexity of the other methods given in this chapter? Suggest a way that
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 variant of A∗ from the previous question?
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 to an iterative-deepening A∗ search that would guarantee this outcome. Why might this be
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 covered, set up as follows:Segment Length Topics covered seg0 10 [welcome]seg1 30 [skiing, views]seg2
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 time. No step may be made into a forbidden shaded area.(a) On the grid shown in Figure 3.13, number
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, finite graphs with loops, and infinite graphs (with finite branching factors).
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. They are very skeptical. Why these functions?Why only these? Explain why a controller requires a
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 the location that is closest to its current position. It should still visit all of the locations.(b)
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 position. Change the controller so that the robot will try to head toward the current location of the
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 to make a more sophisticated robot (e.g., one that follows the wall using the “right-hand
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 position, it may keep circling forever without reaching its target. Design a controller that can detect
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 forever. Change the definition so that the robot goes back to its home and circles there.
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; which one would it have to be, and what would the problem with this be?
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 argument.Choose a particular world, for example, what is on some part of your desk at the current time.i)
Exercise 1.4 Choose four pairs of dimensions that were not covered in the book.For each pair, give one commonsense example of where the dimensions interact.
Exercise 1.3 Find out about two applications of AI (not classes of applications, but specific programs). For each application, write, at most, one typed page describing it. You should try to cover the following questions:(a) What does the application actually do (e.g., control a spacecraft,
Exercise 1.2 Give as many disciplines as you can whose aim is to study intelligent behavior of some sort. For each discipline, find out what aspect of behavior is investigated and what tools are used to study it. Be as liberal as you can regarding what defines intelligent behavior.
Exercise 1.1 For each of the following, give five reasons why:(a) A dog is more intelligent than a worm.(b) A human is more intelligent than a dog.(c) An organization is more intelligent than an individual human.Based on these, give a definition of what “more intelligent” may mean.
10. Look for patterns. Patterns can lend insight to problem solutions.
9. Consider if all the data have been used. It is usually a good idea to see how different data affects a solution.
8. Try to approximate a proposed answer. It is always a good idea to determine if you are in the “right ballpark.”
7. Change the conceptual mode. This is akin to incubation. See if you can look at the problem from a different perspective. This may include restating the problem, drawing diagrams, or choosing alternative representations.
6. See if there is a counterexample that will disprove your hypothesis for a solution.
5. Widen the conditions to see if that will make the problem easier to understand and solve. Methods #4 and # 5 may help in problem comprehension.
4. Narrow the condition. See if the problem can be understood better and solved if the conditions are more restrictive.
3. Word forward from general to specific. In essence, we are trying to use generalizations to reach a specific conclusion. For example, say we encounter one bad student in a school. Then we encounter several bad students in the school. Hence, all the students we have encountered are bad. So when we
2. Work backward from specific to general. That is, take the facts to try to deduce what the nature of the problem is. In AI, this is called
1. Look for a simpler, related problem. This will no doubt lead to a better understanding of the problem itself.
6. Exposition of Donald Michie’s concept of the “Human Window” for solving AI-type problems [8].
5. Recommendations of appropriate strategies and approaches in proper order according to problem type.
4. Study of people’s problem-solving behaviors in a problem-solving setting.
3. Application of appropriate problem-solving strategies to the selected problems as required by the respective problem domains.
2. Discussion of general problem-solving techniques as applicable to the problems under consideration.
1. Classification of the problems under consideration.
What is quantum computing, and what is quantum AI?
How do you develop a mobile AI app?
What is TinyML?
What is edge computing?
What is Docker, and what is Docker Swarm?
Compare the traditional computers, virtual machines, and container-based technologies.
What is Kubernetes? What are containers?
Compare the different Python packaging techniques, such as Nbconvert, Pyinstaller, Py2Exe, Py2app, Auto-Py-To-Exe, Cx_Freeze, Cython, and PIP.
What are Django, Flask, and Streamlit?
What AI services does GCP provide?
What AI services does Azure provide?
What AI services does AWS provide?

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Artificial Intelligence Programming With Python: From Zero To Hero 1st Edition Perry Xiao - Solutions

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