Bayesian Artificial Intelligence 2nd Edition Kevin B. Korb, Ann E. Nicholson - Solutions

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Explain the various steps in knowledge engineering.
What is meant by knowledge?
Explain the various sources of error in expert system development.
Explain prototype construction.
What is meant by formalization?
What are the various stages of the expert system development life cycle?
What is meant by the expert system development life cycle?
Explain the role of the various individuals who interact with an expert system.
Describe the difference between forward and backward chaining.
What is a knowledge base?
What rules do expert systems use?
Describe expertise and its limits.
Define an inference engine used as part of an expert system.
What are the advantages of expert systems over human experts?
What is an expert system?
How does the depth first search get converted into hill climbing?
When will the hill climbing search technique fail? Does the steepest ascent always find solutions? How can some problems be overcome in search?
What is meant by the local maxima with respect to search techniques?
Describe the hill climbing algorithm.
Explain the AO∗ algorithm.
Describe the A∗ algorithm. Prove that the A∗ algorithm is complete and optimal.
Explain the best first search algorithm.
Distinguish between heuristics and algorithms.
Why is the heuristic search better than the blind search?
What are heuristics, and what is their importance?
What are different parameters that are used to evaluate search techniques?
Show that the depth first technique is neither complete nor optimal.
Give the comparisons of the uninformed search strategies.
Explain the DFS and BFS in detail and differentiate between them.
Explain searching for solutions.
What is the search process? What is needed for a search method in AI?
Explain role of AI in big data mining.
What is meant by image processing?
Explain pattern recognition.
What is the importance of artificial intelligence in expert systems?
What is the cognitive modeling approach?
What are intelligence and intelligent agents?
What are the task domains of AI?
What is the Turing test?
What about other comparisons between human and computer intelligence?
Is AI about simulating human intelligence?
Is intelligence a single thing, so that one can ask the “yes or no”question “Is this machine intelligent or not?”
Give an explanation of the difference between a strong AI and weak AI.
What are the various areas where AI (Artificial Intelligence) can be used?
What is Artificial Intelligence?
Explain some ITS system architectures?
Problem 4 In 10.3.1 we described how it is possible to compute the entropy reduction due to evidence about a pair of observations. Write code to do this using the Netica API.
Problem 3 In Table 9.4 we gave a utility function for the missing car problem, for the decision network shown in Figure 9.17, with expected utility results in Table 9.5. Investigate the effects on the decision in the following situations:1. The same relative ordering of outcome preferences is kept,
Problem 2 Start with the decision network for Julia’s manufacturing problem from 4.9. In case it isn’t already, parameterize it so that the decision (between production, marketing, further development) is relatively insensitive to the exact probability of a high vs. low product quality. Then
Problem 1 Prove that is a proper generalization of —in other words, prove that in the binomial case is equivalent to Good’s information reward (assuming that the prior probability is uniform).
Problem 2 Consider the BN that you constructed for your own domain in Problem 5, Chapter 2.(Or if you haven’t completed this problem, use an example network from elsewhere in this text, or one that comes with a BN software package.)1. Identify the type of each node: target/query,
Problem 1 The elicitation method of partitioning to deal with local structure is said in the text to directly correspond to the use of classification trees and graphs in Chapter 7. Illustrate this for the Flu, Measles, Age, Fever example by filling in the partitioned CPT with hypothetical numbers.
Problem 5 Run the causal discovery program on some of the data sets at our book web site. Try the program with and without giving it prior information (such as variable order).Evaluate how well it has done in one of two ways:1. In terms of either predictive accuracy, KL distance or information
Problem 4 Implement the PC algorithm using the ???? from 8.10. Compare the results experimentally with one of the metric causal discovery programs from prior problems.Evaluation Problem For this problem you should get and install one or more of the causal discovery programs: TETRAD IV, WinMine or
Problem 3 Implement a simple greedy search through the dag or TOM space. Test the result on artificial data using one or more metrics from prior problems.
Problem 2 Implement one of the alternative metrics for discrete causal models, namely MDL, or MML (or BDe, after reading [103]).
Problem 1 Implement the Bayesian metric ???? of (8.3) for discrete causal models.
Problem 6 1. Implement maximum likelihood expectation maximization (Algorithm 7.5).2. Implement MAP expectation maximization (Algorithm 7.6).Try your algorithm(s) out using some of the data sets with missing values from the book Web site and report the results. Note that there is Lisp code
Problem 5 Implement the Gibbs Sampling algorithm (7.3) for estimating parameters with incomplete data. The missing Cooper & Herskovits computations can be Ýlled in by copying the Lisp function for them on the bookWeb site, or by skipping forward and implementing Equation (8.3) from Chapter 8. Try
Problem 4 Implement the multinomial parameterization algorithm (7.1). Test it on some of the data sets on the bookWeb site and report the results.
Problem 3 In this problem you will analyze a very simple artiÝcially created data set from the bookWeb site http://www.csse.monash.edu.au/bai.html which was created by a v-structure process ???? ó i.e., one with three variables of which one is the child of the other two which are
Problem 2 Suppose your prior Dirichlet distribution for the roll of a die is ???????? and that you update this distribution as in ????7.2.2. Roll a die ten times and update this. Is the posterior distribution Ðat? How might you get a Ðatter posterior distribution?Experimental
Problem 1 Suppose your prior distribution for the probability of heads of a coin in your pocket is B(2,2). Toss the coin ten times. Assuming you update your distribution as in????7.2.1, what is your posterior distribution? What is the expected value of tossing the coin on your posterior
Problem 6 Reimplement your oracle-based algorithm, whether CI or PC, using signiÝcance testing of partial correlations instead of the oracle, with the input being joint samples across the variables rather than a known Bayesian net. Using a variety of ???? levels(say, 0.01, 0.05, 0.1), compare the
Problem 5 Instead of, or in addition to, the CI algorithm above, implement the PC algorithm, again using the oracle from Chapter 2 (rather than signiÝcance tests). Your PC learning algorithm should take as input a list of variables and then, using the oracle supplied with some Bayesian network,
Problem 4 Implement the CI algorithm using your d-separation oracle. That is, your CI learning algorithm should take as input a list of variables and then, using the oracle supplied with some Bayesian network, discover as much of the Bayesian network structure as possible. It should output the
Problem 3 If you have not already done so, complete the oracle from the problems in Chapter 2.Run your d-separation oracle on the additional test network for the CI learning example, LearningEg.dne, which can be found in http://www.csse.monash.edu.au/bai
Problem 2 Consider the model:Find all the dags which are Markov equivalent. (You can use the CI algorithm to do this.) b f g h
Problem 1 Consider the model:1. Identify all of the active paths between pairs of variables.2. Use Wrightís decomposition rule (in either form) to generate a system of equations for the correlations in terms of the path coefÝcients.3. Solve the system of equations ó i.e., convert it into a set
Problem 7 Design a heart attack Bayesian network which can be used to predict the impact of changes in lifestyle (e.g., smoking, exercise, weight control) on the probability of premature death due to heart attack. You will have to make various choices about the resolution of your variables, for
Problem 5 In ????5.4.4, we saw how the use of a sensor status node can be used to model both intermittent and persistent faults in the sensor. In the text this node had only two states, working and defective, which does not represent the different kinds of faults that may occur, in terms of false
Problem 2 What are the observation nodes when using the Poker network for estimating BPP’s probability of winning? What are the observation nodes when using the network to assess the opponent’s estimate of winning chances?
Problem 6 Peter wants to do simple monitoring of his local weather. Suppose that the main weather types are: sunny, cloudy, showers, rain and snow. He knows that the weather changes from one day to the next in certain patterns, depending on the season. He also has access to the local weather
Problem 3 Robert is trying to decide whether to study hard for the Bayesian Artificial Intelligence exam. He would be happy with a good mark (e.g., a High Distinction) for the subject, but he knows that his mark will depend not only on how hard he studies but also on how hard the exam is and how
Problem 2 You have 20 utiles worth of chocolate. We offer to make a gamble with you: tossing some coin, if it comes up heads, weíll give you 130 more utiles worth of chocolate;if it comes up tails, weíll take your 20 utiles worth of chocolate. Assuming that you maximize your expected utility and
Problem 1 You have 20 pounds of cheese. We offer to make a gamble with you: tossing a fair coin, if it comes up heads, weíll give you 130 more pounds of cheese; if it comes up tails, weíll take your 20 pounds of cheese. What is the expected value of the gamble?Assuming that you maximize your
Problem 6 Take Pearl’s earthquake example. Suppose there is an intervention on JohnCalls.1. Use ordinary observation and updating. What is Bel(Burglary)?2. Use Pearl’s cut-link method. What is Bel(Burglary)? this case?3. Use the augmented model with 0.9 effectiveness. What is Bel(Burglary)?Now
Problem 5 As mentioned in the BibliographicNotes, Hugin implements an approximation scheme that involves setting very small probabilities in the junction tree tables to zero, which in turns allows more compression to take place.To turn on the approximation scheme, select NetworkNetwork
Problem 4 The on-line material for this text provides a version of both the Logic Sampling algorithm (Algorithm 3.3) and the LikelihoodWeighting algorithm (Algorithm 3.4).Take an example BN (either provided with the online material, or that you’ve developed for the problems set in Chapter 2) and
Problem 3 Consider the BNs for the “Asia” problem described in ????2.5.3.1. Use the Jensen junction tree algorithm (Algorithm 3.2) to construct a junction tree from both these networks, drawing (by hand) the resultant junction trees.2. Load these networks (asia1.dne and asia2.dne in the on-line
Problem 2 A description of how to enter so-called likelihood evidence in both Netica and Hugin software is given in Figure 3.15. In ????3.4 we only looked at an example where we added virtual evidence for a root node; however it can be added for any node in the network. Consider the cancer example
Problem 1 Consider (again — see Problem 2.8, Chapter 2) the belief network for another version of the medical diagnosis example, where B=Bronchitis, S=Smoker, C=Cough, X=Positive X-ray and L=Lung cancer and all nodes are Booleans. Suppose that the prior for a patient being a smoker is 0.25, and
Given the internal Bayesian network structure N, implement a d-separation oracle which, for any three sets of nodes input to it, X, Y, and Z, returns:???? true if ???? ???? (i.e., Z d-separates X and Y in N);???? false if ???????? ???? (i.e., Z does not d-separate X and Y in N);???? some
Consider the Bayesian network given for the previous problem.1. What variable ordering(s) could have been used to produce the above network using the network construction algorithm (Algorithm 2.1)?2. Given different variable orderings, what network structure would result from this algorithm? Use
Come up with your own problem involving reasoning with evidence and uncertainty.Write down a text description of the problem, then model it using a Bayesian network.Make the problem sufficiently complex that your network has at least 8 nodes and is multiply-connected (i.e., not a tree or a
“Jason the Juggler.” Jason, the robot juggler, drops balls quite often when its battery is low. In previous trials, it has been determined that when its battery is low it will drop the ball 9 times out of 10. On the other hand when its battery is not low, the chance that it drops a ball is much
“Fred’s LISP dilemma.” Fred is debugging a LISP program. He just typed an expression to the LISP interpreter and now it will not respond to any further typing.He can’t see the visual prompt that usually indicates the interpreter is waiting for further input. As far as Fred knows, there are
(Note: this is the case of Sally Clark, convicted in the UK in 1999, and found innocent on appeal in 2003; see [120].) A mother was arrested after her second baby died a few months old, apparently of sudden infant death syndrome (SIDS), exactly as her first child had died a year earlier. According
You are offered a Keno ticket in a casino which will pay you $1 million if you win!It only costs you $1 to buy the ticket. You choose 4 numbers out of a 9x9 grid of distinct numbers. You win if all of your 4 numbers come up in a random draw of four from the 81 numbers. What is the expected dollar
Construct a Dutch book against someone who violates the Axiom of Additivity. That is, suppose a Mr. Fuzzy declares about the weather tomorrow that ???????? ???????? ???????? . Mr. Fuzzy and you agree about what will count as sunny and as inclement
(Adapted from [83].) Two cab companies, the Blue and the Green, operate in a given city. Eighty-five percent of the cabs in the city are Blue; the remaining 15% are Green. A cab was involved in a hit-and-run accident at night. A witness identified the cab as a Green cab. The court tested the
You consider the probability that a coin is double-headed to be 0.01 (call this option????????); if it isn’t double-headed, then it’s a fair coin (call this option ????). For whatever reason, you can only test the coin by flipping it and examining the coin (i.e., you can’t simply examine both
The frequency of male births at the Royal Women’s Hospital is about 51 in 100. On a particular day, the last eight births have been female. The probability that the next birth will be male is:1. About 51%2. Clearly greater than 51%3. Clearly less than 51%4. Almost certain 5. Nearly zero Bayes’
Prove the theorems of 1.3.1, namely the Total Probability theorem and the Chain Rule.
Given that two pieces of evidence ???? and are conditionally independent given the hypothesis—i.e., ???????????? ????????????—prove the “product rule”: ???????????? ???????????? ????????.
Prove that the conditional probability function ????????????, if well defined, is a probability function (i.e., satisfies the three axioms of Kolmogorov).
In x10.3.7.1 we described how it is possible to compute the entropy reduction due to evidence about a pair of observations. Write code to do this using the Netica API.
Problem 6 In x10.4.8.1 we described how it is possible to compute the entropy reduction due to evidence about a pair of observations. Write code to do this using the Netica API.
Problem 5 In Table 10.9 we gave a utility function for the missing car problem, for the decision network shown in Figure 10.29, with expected utility results in Table 10.10.Investigate the effects on the decision in the following situations:1. The same relative ordering of outcome preferences is
Problem 4 Start with the decision network for Julia’s manufacturing problem from x4.10. In case it isn’t already, parameterize it so that the decision (between production, marketing, further development) is relatively insensitive to the exact probability of a high vs. low product quality. Then

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Bayesian Artificial Intelligence 2nd Edition Kevin B. Korb, Ann E. Nicholson - Solutions

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