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Artificial Intelligence Structures And Strategies For Complex Problem Solving 5th Edition George F. Luger - Solutions

Do a to e as in Exercise 6 for the missionary and cannibal problem:Three missionaries and three cannibals come to the bank of a river they wish to cross. There is a boat that will hold only two, and any of the group is able to row. If there are ever more missionaries than cannibals on any side of
Write the PROLOG code for the farmer, wolf, goat, and cabbage problem, Section 15.3.a. Execute this code and draw a graph of the search space.b. Alter the rule ordering to produce alternative solution paths.c. Use the shell in the text to produce a breadth-first problem.d. Describe a heuristic that
Write a PROLOG program to count the elements in a list (a list within the list counts as one element). Write a program to count the atoms in a list (count the elements within any sublist). Hint: several meta-predicates such as atom() can be helpful.
Design a PROLOG program unique(Bag, Set) that takes a Bag (a list that may contain duplicate elements) and returns a Set (no elements are repeated).
Write the “member check” program in PROLOG. What happens when an item is not in the list? Query to the “member” specification to break a list into its component elements.
Write a PROLOG program to answer Wirth’s “I am my own grandfather” problem (Chapter 2, Exercise 12).
Create a relational database in PROLOG. Represent the data tuples as facts and the constraints on the data tuples as rules. Suitable examples might be from stock in a department store or records in a personnel office.
As a project, build an information extraction system for some knowledge domain to be used on the WWW. See Section 14.5 for suggestions.
Use of the stochastic approach for discovering patterns in a relational database is an important area of current research, sometime referred to as data mining (see Section 14.3).How might this work be used to answer queries, such as those posed in Section 14.5 about relational databases?
How might the stochastic approaches of Section 14.4 be combined with the techniques for database analysis found in Section 14.5.
Assume that managers are listed in the employee_salary relation with other employees in the example of Section 14.5.2. Extend the example so that it will handle queries such as“Find any employee that earns more than his or her manager.”
Take the previous problem and put its words, including punctuation, in random order.
Extend the database front end example of Section 14.5.2 so that it will answer questions of the form “How much does Don Morrison earn?” You will need to extend the grammar, the representation language, and the knowledge base.
Describe how the Markov models of Section 14.4 might be combined with the more symbolic approach to understanding language of Sections 14.1−14.3.
Expand the ATN grammar of Section 14.2.4 to include who and what questions.
Define the procedures for building a semantic representation from the parse tree.
Define concepts and relations in conceptual graphs needed to represent the meaning of the grammar of Exercise
Define an ATN parser for the dogs world grammar with adjectives (Exercise 7) and prepositional phrases (Exercise 8).
Add the following context-free grammar rules to the dogs world grammar of Section 14.2.1.Map the resulting grammar into transition networks.sentence ↔ noun_phrase verb_phrase prepositional_phrase prepositional_phrase ↔ preposition noun_phrase preposition ↔ with preposition ↔ to preposition
Extend the dogs world grammar to include adjectives in noun phrases. Be sure to allow an indeterminate number of adjectives. Hint: use a recursive rule, adjective_list, that either is empty or contains an adjective followed by an adjective list. Map this grammar into transition networks.
Produce a parse tree for each of the following sentences. You will have to extend our simple grammars with more complex linguistic constructs such as adverbs, adjectives, and prepositional phrases. If a sentence has more than one parsing, diagram all of them and explain the semantic information
Parse each of these sentences using the context-sensitive grammar of Section 14.2.3.The men like the dog.The dog bites the man.
Extend the dogs world grammar so it will include the illegal sentences in Exercise 3.
Parse each of these sentences using the “dogs world” grammar of Section 14.2.1. Which of these are illegal sentences? Why?The dog bites the dog.The big dog bites the man.Emma likes the boy.The man likes.Bite the man.
Discuss the representational structures and knowledge necessary to understand the following sentences.The brown dog ate the bone.Attach the large wheel to the axle with the hex nut.Mary watered the plants.The spirit is willing but the flesh is weak.My kingdom for a horse!
Classify each of the following sentences as either syntactically incorrect, syntactically correct but meaningless, meaningful but untrue, or true. Where in the understanding process is each of these problems detected?Colorless green ideas sleep furiously.Fruit flies like a banana.Dogs the bite man
An alternative semantic model for logic programming is that of Flat Concurrent PROLOG.Compare the semantics of PROLOG seen in Section 13.3 with that of Flat Concurrent PROLOG (Shapiro 1987).
Derive a resolution proof of the theorem of Figure 13.1.
Use factoring and resolution to produce a refutation for the following clauses: p(X) ∨p(f(Y)) and ¬ p(W) ∨ ¬ p(f(Z)). Try to produce a refutation without factoring.
Create the and/or graph for the following problem. Why is it impossible to conclude the goal:r(Z) ∨ s(Z)?Fact: p(X) ∨ q(X).Rules: p(a) → r(a) and q(b) → s(b).
Prove the linear input form strategy is not refutation complete.
Create the and/or graph for the following data-driven predicate calculus deduction.Fact: ¬ d(f) ∨ [b(f) ∧ c(f)].Rules: ¬ d(X) → ¬ a(X) and b(Y) → e(Y) and g(W) ← c(W).Prove: ¬ a(Z) ∨ e(Z).
Put the following predicate calculus expression in clause form:∀ (X)(p(X) → {∀ (Y)[p(Y) → p(f(X,Y))] ∧ ¬ ∀ (Y)[q(X,Y) → p(Y)]})
Take the happy student problem of Figure 13.5 and apply three of the refutation strategies of Section 13.2.4 to its solution.
Pick a “canonical set” of six family relations. Write demodulators to reduce alternative forms of relations to the set. For example, your “mother’s brother” is “uncle.”
Write a demodulator for sum that would cause clauses of the form equal(ans, sum(5, sum(6, minus(6)))) to be reduced to equal(ans, sum(5, 0)). Write a further demodulator to reduce this last result to equal(ans, 5).
Work out two examples for hyperresolution where the nucleus has at least four literals.
Use resolution to solve the following puzzle problem from Wos et al. (1984). There are four people: Roberta, Thelma, Steve, and Pete. The four hold eight different jobs. Each person has exactly two jobs. The jobs are, without sex bias, chef, guard, nurse, telephonist, police officer, teacher,
Use resolution for queries in the farmer, wolf, goat, and cabbage problem of Section 15.3.
What problems might arise in a large problem space?
How would you do data-driven reasoning with resolution? Use this to address the search space of Exercise
How might you use resolution to implement a “production system” search?
In Chapter 6 we presented a simplified form of the knight’s tour. Take the path3 rule, put it in clause form, and use resolution to answer queries such as path3(3,6). Next, use the recursive path call, in clause form, to answer queries.
Use resolution to answer the query in Example 3.3.4.
Use resolution to prove Wirth’s statement in Exercise 12, Chapter 2.
Take the logic-based financial advisor of Section 2.4, put the predicates describing the problem into clause form, and use resolution refutations to answer queries such as whether a particular investor should make an investment(combination).
For further insights into evolution and the emergence of complexity, read and discuss Darwin’s Dangerous Idea (Dennett 1995) or Full House: The Spread of Excellence from Plato to Darwin (Gould 1996).
Is this issue resolvable? That is, does the genetic model of learning work solely because of its representational assumptions or can it be translated into broader domains?
Discuss the role of inductive bias in the representations, search strategies, and operators used in the models of learning presented in Chapter
The area of agent-based research was introduced in Section 12.3. We recommend further reading on any of the projects mentioned, but especially the Brooks, Nilsson and Benson, or Crutchfield and Mitchell research. Write a short paper on one of these topics.
Write an a-life program that implements the functionality of Figures 12.10 –12.13.
Read the early discussion of the Game of Life in Gardner’s column of Scientific American(1970, 1971). Discuss other a-life structures, similar to the glider, presented in Section 12.3.1.
Discuss the constraints (presented in Section 12.2.2) on using genetic programming techniques to solve problems. For example, what components of a solution cannot be evolved within the genetic programming paradigm?
Write a program to solve Kepler’s Third Law of Motion Problem, described with a preliminary representation offered in Section 12.2.2.
How does the Bucket Brigade Algorithm (Holland 1986) relate to the backpropagation algorithm (Section 14.3)?
Read Holland’s Schema Theorem (Mitchell 1996, Koza 1992). How does Holland’s schema theory describe the evolution of the GA solution space? What does it have to say about problems not encoded as bit strings?
Discuss the role of representational techniques such as gray coding for shaping the search space for the genetic algorithm. Discuss two other problem domains where similar techniques will be important.
Build a genetic algorithm to search for a solution for the traveling salesperson problem.
Consider the traveling salesperson problem of Section 12.1.1. Discuss the problem of selecting an appropriate representation for this problem. Design other appropriate genetic operators and fitness measures for this problem.
Build a genetic algorithm to solve the CNF-satisfaction problem.
Consider the CNF-satisfaction problem of Section 12.1.1. How does the role of the number of disjuncts in the CNF expression bias the solution space? Consider other possible representations and genetic operators for the CNF-satisfaction problem. Can you design another fitness measure?
Discuss the problem of designing representations for genetic operators to search for solutions in different domains? What is the role of inductive bias here?
The genetic algorithm is intended to support the search for genetic diversity along with the survival of important skills (represented by genetic patterns) for a problem domain. Describe how different genetic operators can simultaneously support both these goals.
Write a Hopfield net to solve the traveling salesperson problem for ten cities.
Describe the differences between the BAM memory and the linear associator. What is crosstalk and how can it be prevented?
Consider the bidirectional associative memory (BAM) of Section 11.6.3. Change the association pairs given in our example and create the weight matrix for the associations.Select new vectors and test your BAM associator.
Section 11.5.4 used the linear associator algorithm to make two vector pair associations.Select three (new) vector pair associations and solve the same task. Test whether your linear associator is interpolative; that is, can it associate near misses of the exemplars? Make your linear associator
Select a different input pattern than that we used in Section 11.5.2. Use the unsupervised Hebbian learning algorithm to recognize that pattern.
This 24 element vector would be the input value for your net. You would build your own training vectors. Do the same task with a counterpropagation net; compare your results.
Use a backpropagation net to recognize the ten (hand drawn) digits. One approach would be to build a 4 x 6 array of points. When a digit is drawn on this grid it will cover some elements, giving them value 1, and miss others, value
Write a counterpropagation net to solve the exclusive-or problem. Compare your results with those of the backpropagation net of Section 11.3.3. Use your counterpropagation net to discriminate between the classes of Table 11.3.
Write a Kohonen net in LISP or C++ and use it to classify the data of Table 11.3. Compare your results with those of Sections 11.2.2 and 11.4.2.
Build a backpropagation network in LISP or C++ and use it to solve the exclusive-or problem of Section 11.3.3. Solve the exclusive-or problem with a different backpropagation architecture, perhaps having two hidden nodes and no bias nodes. Compare the convergence speeds using the different
Build a perceptron net in LISP and run it on the classification example of Section 11.2.2.a. Generate another data set similar to that of Table 11.3 and run your classifier on it.b. Take the results of running the classifier and use the weights to determine the specification for the line separating
Make a McCulloch−Pitts neuron that can calculate the logic function implies, ⇒.
Another problem type excellent for reinforcement learning is the so-called gridworld. We present a simple 4 × 4 gridworld in Figure 10.26. The two greyed corners are the desired terminal states for the agent. From all other states, agent movement is either up, down, left, or right. The agent
Can you analyze the inverted pendulum problem, Figure 9.8, presented in Section 9.2.2 from a reinforcement learning perspective? Build some simple reward measures and use the temporal difference algorithm in your analysis.
Analyze Samuel’s checker playing program from a reinforcement learning perspective.Sutton and Barto (1998, Section 11.2) offer suggestions in this analysis.
What happens if the temporal difference algorithm of Problem 13 plays tic-tac-toe against itself?
Consider the tic-tac-toe example of Section 10.7.2. Implement the temporal difference learning algorithm in the language of your choice. If you designed the algorithm to take into account problem symmetries, what do you expect to happen? How might this limit your solution?
Develop an explanation-based learning algorithm in the language of your choice. If you use PROLOG, consider the algorithms developed in Section 15.8.3.
Develop a domain theory for explanation-based learning in some problem area of your choice. Trace the behavior of an explanation-based learner in applying this theory to several training instances.
From Quinlan (1993) obtain the C4.5 decision tree algorithm and test it on a data set.There are complete programs and data sets for C4.5 available from this reference.
Other problems of ID3 are bad or missing data. Data is bad if one set of attributes has two different outcomes. Data is missing if part of the attribute is not present, perhaps because it was too expensive to obtain. How might these issues be dealt with in development of ID3 algorithms?
Discuss problems that can arise from using continuous attributes in data, such as a monetary cost, dollars and cents, or the height, a real number, of an entity. Suggest some method for addressing this problem of continuous data.
Implement ID3 in a language of your choice and run it on the credit history example from the text. If you use LISP, consider the algorithms and data structures developed in Section 16.13 for suggestions.
Develop a simple table of examples in some domain, such as classifying animals by species, and trace the construction of a decision tree by the ID3 algorithm.
Using Shannon’s formula, show whether or not a message about the outcome of a spin of a roulette wheel has more information than one about the outcome of a coin toss. What if the roulette wheel message is “not 00”?
Using the information theoretic selection function of Section 10.4.3, show in detail how ID3 constructs the tree of Figure 10.14 from examples in Table 10.1. Be sure to show the calculations used in computing the information gain for each test and the resulting test selections.
Build the version space search algorithms in PROLOG, or the language of your choice. If you use PROLOG, see hints in Section 15.14.1.
The run of the candidate elimination algorithm shown in Figure 10.9 does not show candidate concepts that were produced but eliminated because they were either overly general, overly specific, or subsumed by some other concept. Re-do the execution trace, showing these concepts and the reasons each
Consider the behavior of Winston’s concept learning program when learning the concept“step,” where a step consists of a short box and a tall box placed in contact with each other, as in Figure 10.25. Create semantic net representations of three or four examples and near misses and show the
Given that you wanted to design a second-order Markov model, i.e., where each observable state would be dependent on the previous two observable states. How would you do this?What would the transition probability matrix look like?
Take the diagnostic reasoning situation developed in Tables 9.1 and 9.2 of the Dempster–Shafer model of Section 9.2.3 and recast it as a Bayesian belief network. Compare and contrast these two approaches to diagnosis.
Create cliques and a junction tree for the following situation (seen in Figure 9.23). Robbery, vandalism and an earthquake can all set off (cause) a house alarm. There is also a measure of the potential dangers in the neighborhood of the house.
Create a Bayesian belief diagram for another application, for example medical diagnosis, geological discovery, or automobile fault analysis. Point out examples of d-separation and create a clique tree for this network.
Create an algorithm for Bayesian belief propagation and apply it to the slippery sidewalk domain of Section 9.3.2. You might use Pearl’s (1988) message passing approach or the clique triangulation method proposed by Lauritzen and Spiegelhalter (1988).
Complete the symbolic evaluations that are required to finish Table 9.4.
Put another link in Figure 9.16, say connecting season directly to slick sidewalk and then create a clique tree to represent this situation. Compare the complexity issues with those of the clique tree of Figure 9.17.
Go to the literature, for example Ross (1995), and describe two other areas where fuzzy control might be appropriate. Construct a set of fuzzy rules for those domains.

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