ion: Consider the following rules " If one is drunk or sick then he/she is not sober. Further, assume the following facts concerning the respective people: "Tony is sober" "Tom is not sober" "Esther is sick" "Alice is not sick and is not drunk" "John is very principled. He can never be drunk when sick"

U.S.I.J.B Required: State whether each of the following conclusions is TRUE or FALSE according to the above rules and facts based on the rules of propositioned logic (i) "Tony is not drunk and is not sick" (ii) "Either Tony is not drunk or is not sick" (iii) "Tom is either drunk or sick" (iv)"Alice is sober" (v) "John is never sick when drunk" Evolutionary Computing (EC) = Genetic Programming (GP) (1992)+ Evolution strategies (ES)(1965) + Evolutionary programming (EP) (1962)+ Genetic Algorithms (GA)(1970) Genetic algorithms computing. (Gas) are the main paradigm of evolutionary GAs are inspired by Darwin's theory about evolution - the "survival of the fittest". In nature, competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones. Gas are the ways of solving problems by mimicking processes nature uses; ie., Selection, Crosses over, Mutation and Accepting, to evolve a solution to a problem. Gas are adaptive heuristic search based on the evolutionary ideas of natural selection and genetics. GAs are intelligent exploitation of random search used in optimization problems. GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space. Pseudo-Code BEGIN INITIALISE population with random candidate solution. EVALUATE each candidate; REPEAT UNTIL (termination condition ) is satisfied DO 1. SELECT parents; 2. RECOMBINE pairs of parents; 3. MUTATE the resulting offspring; 4. SELECT individuals or the next generation; END. Working Principles Chromosome : a set of genes; a chromosome contains the solution in form of genes. Gene : a part of chromosome; a gene contains a part of solution. It determines the solution. e.g. 16743 is a chromosome and 1, 6, 7, 4 and 3 are its genes. Individual : same as chromosome. Population: of number individuals present with same length of chromosome. Fitness : the value assigned to an individual based on how far or close a individual is from the solution; greater the fitness value better the solution it contains. Fitness function : a function that assigns fitness value to the individual. It is problem specific. Breeding : taking two fit individuals and then intermingling there chromosome to create new two individuals.Consider the following rules " If one is drunk or sick then he/she is not sober. Further, assume the following facts concerning the respective people: "Tony is sober" "Tom is not sober" "Esther is sick" "Alice is not sick and is not drunk" "John is very principled. He can never be drunk when sick" Required: State whether each of the following conclusions is TRUE or FALSE according to the above rules and facts based on the rules of propositioned logic (i) "Tony is not drunk and is not sick" (ii) "Either Tony is not drunk or is not sick" (iii) "Tom is either drunk or sick" (iv)"Alice is sober" (v) "John is never sick when drunk" Evolutionary Computing (EC) = Genetic Programming (GP) (1992)+ Evolution strategies (ES)(1965) + Evolutionary programming (EP) (1962)+ Genetic Algorithms (GA)(1970) Genetic algorithms computing. (Gas) are the main paradigm of evolutionary GAs are inspired by Darwin's theory about evolution - the "survival of the fittest". In nature, competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones. Gas are the ways of solving problems by mimicking processes nature uses; ie., Selection, Crosses over, Mutation and Accepting, to evolve a solution to a problem. Gas are adaptive heuristic search based on the evolutionary ideas of natural selection and genetics. GAs are intelligent exploitation of random search used in optimization problems. GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space. Pseudo-Code BEGIN INITIALISE population with random candidate solution. EVALUATE each candidate; REPEAT UNTIL (termination condition ) is satisfied DO 1. SELECT parents; 2. RECOMBINE pairs of parents; 3. MUTATE the resulting offspring; 4. SELECT individuals or the next generation; END. Working Principles Chromosome : a set of genes; a chromosome contains the solution in form of genes. Gene : a part of chromosome; a gene contains a part of solution. It determines the solution. e.g. 16743 is a chromosome and 1, 6, 7, 4 and 3 are its genes. Individual : same as chromosome. Population: of number individuals present with same length of chromosome. Fitness : the value assigned to an individual based on how far or close a individual is from the solution; greater the fitness value better the solution it contains. Fitness function : a function that assigns fitness value to the individual. It is problem specific. Breeding : taking two fit individuals and then intermingling there chromosome to create new two individuals. Mutation : changing a random gene in an individual. Selection : selecting individuals for creating the next generation. Procedure Genetic algorithm begins with a set of solutions (represented by chromosomes) called the population. Solutions from one population are taken and used to form a new population. This is motivated by the possibility that the new population will be better than the old one. Solutions are selected according to their fitness to form new solutions (offspring); more suitable they are, more chances they have to reproduce. This is repeated until some condition (e.g. number of populations or improvement of the best solution) is satisfied. Operators of Genetic Algorithm Genetic operators used in genetic algorithms maintain genetic diversity. Genetic diversity or variation is a necessity for the process of evolution. Genetic operators are analogous to those which occur in the natural world: Reproduction (or Selection) ; Crossover (or Recombination); and Mutation. In addition to these operators, there are some parameters of GA. One important parameter is Population size. Population size says how many chromosomes are in population (in one generation). If there are only few chromosomes, then GA would have a few possibilities to perform crossover and only a small part of search space is explored. If there are many chromosomes, then GA slows down. Research shows that after some limit, it is not useful to increase population size, because it does not help in solving the problem faster. The population size depends on the type of encoding and the problem. Consider the following rules " If one is drunk or sick then he/she is not sober. Further, assume the following facts concerning the respective people: "Tony is sober" "Tom is not sober" "Esther is sick" "Alice is not sick and is not drunk" "John is very principled. He can never be drunk when sick" Required: State whether each of the following conclusions is TRUE or FALSE according to the above rules and facts based on the rules of propositioned logic (i) "Tony is not drunk and is not sick" (ii) "Either Tony is not drunk or is not sick" (iii) "Tom is either drunk or sick" (iv)"Alice is sober" (v) "John is never sick when drunk" Evolutionary Computing (EC) = Genetic Programming (GP) (1992)+ Evolution strategies (ES)(1965) + Evolutionary programming (EP) (1962)+ Genetic Algorithms (GA)(1970) Genetic algorithms computing. (Gas) are the main paradigm of evolutionary GAs are inspired by Darwin's theory about evolution - the "survival of the fittest". In nature, competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones. Gas are the ways of solving problems by mimicking processes nature uses; ie., Selection, Crosses over, Mutation and Accepting, to evolve a solution to a problem. Gas are adaptive heuristic search based on the evolutionary ideas of natural selection and genetics. GAs are intelligent exploitation of random search used in optimization problems. GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space. Pseudo-Code BEGIN INITIALISE population with random candidate solution. EVALUATE each candidate; REPEAT UNTIL (termination condition ) is satisfied DO 1. SELECT parents; 2. RECOMBINE pairs of parents; 3. MUTATE the resulting offspring; 4. SELECT individuals or the next generation; END. Working Principles Chromosome : a set of genes; a chromosome contains the solution in form of genes. Gene : a part of chromosome; a gene contains a part of solution. It determines the solution. e.g. 16743 is a chromosome and 1, 6, 7, 4 and 3 are its genes. Individual : same as chromosome. Population: of number individuals present with same length of chromosome. Fitness : the value assigned to an individual based on how far or close a individual is from the solution; greater the fitness value better the solution it contains. Fitness function : a function that assigns fitness value to the individual. It is problem specific. Breeding : taking two fit individuals and then intermingling there chromosome to create new two individuals. Mutation : changing a random gene in an individual. Selection : selecting individuals for creating the next generation. Procedure Genetic algorithm begins with a set of solutions (represented by chromosomes) called the population. Solutions from one population are taken and used to form a new population. This is motivated by the possibility that the new population will be better than the old one. Solutions are selected according to their fitness to form new solutions (offspring); more suitable they are, more chances they have to reproduce. This is repeated until some condition (e.g. number of populations or improvement of the best solution) is satisfied. Operators of Genetic Algorithm Genetic operators used in genetic algorithms maintain genetic diversity. Genetic diversity or variation is a necessity for the process of evolution. Genetic operators are analogous to those which occur in the natural world: Reproduction (or Selection) ; Crossover (or Recombination); and Mutation. In addition to these operators, there are some parameters of GA. One important parameter is Population size. Population size says how many chromosomes are in population (in one generation). If there are only few chromosomes, then GA would have a few possibilities to perform crossover and only a small part of search space is explored. If there are many chromosomes, then GA slows down. Research shows that after some limit, it is not useful to increase population size, because it does not help in solving the problem faster. The population size depends on the type of encoding and the problem.

Mutation : changing a random gene in an individual. Selection : selecting individuals for creating the next generation. Procedure Genetic algorithm begins with a set of solutions (represented by chromosomes) called the population. Solutions from one population are taken and used to form a new population. This is motivated by the possibility Consider the following rules " If one is drunk or sick then he/she is not sober. Further, assume the following facts concerning the respective people: "Tony is sober" "Tom is not sober" "Esther is sick" "Alice is not sick and is not drunk" "John is very principled. He can never be drunk when sick" Required: State whether each of the following conclusions is TRUE or FALSE according to the above rules and facts based on the rules of propositioned logic (i) "Tony is not drunk and is not sick" (ii) "Either Tony is not drunk or is not sick" (iii) "Tom is either drunk or sick" (iv)"Alice is sober" (v) "John is never sick when drunk" Evolutionary Computing (EC) = Genetic Programming (GP) (1992)+ Evolution strategies (ES)(1965) + Evolutionary programming (EP) (1962)+ Genetic Algorithms (GA)(1970) Genetic algorithms computing. (Gas) are the main paradigm of evolutionary GAs are inspired by Darwin's theory about evolution - the "survival of the fittest". In nature, competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones. Gas are the ways of solving problems by mimicking processes nature uses; ie., Selection, Crosses over, Mutation and Accepting, to evolve a solution to a problem. Gas are adaptive heuristic search based on the evolutionary ideas of natural selection and genetics. GAs are intelligent exploitation of random search used in optimization problems. GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space. Pseudo-Code BEGIN INITIALISE population with random candidate solution. EVALUATE each candidate; REPEAT UNTIL (termination condition ) is satisfied DO 1. SELECT parents; 2. RECOMBINE pairs of parents; 3. MUTATE the resulting offspring; 4. SELECT individuals or the next generation; END. Working Principles Chromosome : a set of genes; a chromosome contains the solution in form of genes. Gene : a part of chromosome; a gene contains a part of solution. It determines the solution. e.g. 16743 is a chromosome and 1, 6, 7, 4 and 3 are its genes. Individual : same as chromosome. Population: of number individuals present with same length of chromosome. Fitness : the value assigned to an individual based on how far or close a individual is from the solution; greater the fitness value better the solution it contains. Fitness function : a function that assigns fitness value to the individual. It is problem specific. Breeding : taking two fit individuals and then intermingling there chromosome to create new two individuals. Mutation : changing a random gene in an individual. Selection : selecting individuals for creating the next generation. Procedure Genetic algorithm begins with a set of solutions (represented by chromosomes) called the population. Solutions from one population are taken and used to form a new population. This is motivated by the possibility that the new population will be better than the old one. Solutions are selected according to their fitness to form new solutions (offspring); more suitable they are, more chances they have to reproduce. This is repeated until some condition (e.g. number of populations or improvement of the best solution) is satisfied. Operators of Genetic Algorithm Genetic operators used in genetic algorithms maintain genetic diversity. Genetic diversity or variation is a necessity for the process of evolution. Genetic operators are analogous to those which occur in the natural world: Reproduction (or Selection) ; Crossover (or Recombination); and Mutation. In addition to these operators, there are some parameters of GA. One important parameter is Population size. Population size says how many chromosomes are in population (in one generation). If there are only few chromosomes, then GA would have a few possibilities to perform crossover and only a small part of search space is explored. If there are many chromosomes, then GA slows down. Research shows that after some limit, it is not useful to increase population size, because it does not help in solving the problem faster. The population size depends on the type of encoding and the problem. that the new population will be better than the old one. Solutions are selected according to their fitness to form new solutions (offspring); more suitable they are, more chances they have to reproduce. This is repeated until some condition (e.g. number of populations or improvement of the best solution) is satisfied. Operators of Genetic Algorithm Genetic operators used in genetic algorithms maintain genetic diversity. Genetic diversity or variation is a necessity for the process of evolution. Genetic operators are analogous to those which occur in the natural world: Reproduction (or Selection) ; Crossover (or Recombination); and Mutation. In addition to these operators, there are some parameters of GA. One important parameter is Population size. Population size says how many chromosomes are in population (in one generation). If there are only fConsider the following rules " If one is drunk or sick then he/she is not sober. Further, assume the following facts concerning the respective people: "Tony is sober" "Tom is not sober" "Esther is sick" "Alice is not sick and is not drunk" "John is very principled. He can never be drunk when sick" Required: State whether each of the following conclusions is TRUE or FALSE according to the above rules and facts based on the rules of propositioned logic (i) "Tony is not drunk and is not sick" (ii) "Either Tony is not drunk or is not sick" (iii) "Tom is either drunk or sick" (iv)"Alice is sober" (v) "John is never sick when drunk" Evolutionary Computing (EC) = Genetic Programming (GP) (1992)+ Evolution strategies (ES)(1965) + Evolutionary programming (EP) (1962)+ Genetic Algorithms (GA)(1970) Genetic algorithms computing. (Gas) are the main paradigm of evolutionary GAs are inspired by Darwin's theory about evolution - the "survival of the fittest". In nature, competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones. Gas are the ways of solving problems by mimicking processes nature uses; ie., Selection, Crosses over, Mutation and Accepting, to evolve a solution to a problem. Gas are adaptive heuristic search based on the evolutionary ideas of natural selection and genetics. GAs are intelligent exploitation of random search used in optimization problems. GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space. Pseudo-Code BEGIN INITIALISE population with random candidate solution. EVALUATE each candidate; REPEAT UNTIL (termination condition ) is satisfied DO 1. SELECT parents; 2. RECOMBINE pairs of parents; 3. MUTATE the resulting offspring; 4. SELECT individuals or the next generation; END. Working Principles Chromosome : a set of genes; a chromosome contains the solution in form of genes. Gene : a part of chromosome; a gene contains a part of solution. It determines the solution. e.g. 16743 is a chromosome and 1, 6, 7, 4 and 3 are its genes. Individual : same as chromosome. Population: of number individuals present with same length of chromosome. Fitness : the value assigned to an individual based on how far or close a individual is from the solution; greater the fitness value better the solution it contains. Fitness function : a function that assigns fitness value to the individual. It is problem specific. Breeding : taking two fit individuals and then intermingling there chromosome to create new two individuals. Mutation : changing a random gene in an individual. Selection : selecting individuals for creating the next generation. Procedure Genetic algorithm begins with a set of solutions (represented by chromosomes) called the population. Solutions from one population are taken and used to form a new population. This is motivated by the possibility that the new population will be better than the old one. Solutions are selected according to their fitness to form new solutions (offspring); more suitable they are, more chances they have to reproduce. This is repeated until some condition (e.g. number of populations or improvement of the best solution) is satisfied. Operators of Genetic Algorithm Genetic operators used in genetic algorithms maintain genetic diversity. Genetic diversity or variation is a necessity for the process of evolution. Genetic operators are analogous to those which occur in the natural world: Reproduction (or Selection) ; Crossover (or Recombination); and Mutation. In addition to these operators, there are some parameters of GA. One important parameter is Population size. Population size says how many chromosomes are in population (in one generation). If there are only few chromosomes, then GA would have a few possibilities to perform crossover and only a small part of search space is explored. If there are many chromosomes, then GA slows down. Research shows that after some limit, it is not useful to increase population size, because it does not help in solving the problem faster. The population size depends on the type of encoding and the problem. ew chromosomes, then GA would have a few possibilities to perform crossover and only a small part of search space is explored. If there are many chromosomes, then GA slows down. Research shows that after some limit, it is not useful to increase population size, because it does not help in solving the problem faster. The population size depends on the type of encoding and the problem. Evil Robot hates kittens. He has invented the kitty-destroyer (KD) to rid the world of their menace. To test it, he has a kitten (K) next to the KD on his laboratory bench (B). He has to open the KD, place the kitten inside it, close it and press the start button (SB). He has not however established that this sequence of events will lead to his goal of a destroyed kitten. Evil Robot is equipped with a planning system based on a solver for constraint satisfaction problems, and wants to use this to construct a plan. 1. Explain how this problem can be represented using the state-variable representation, including in your answer specific examples of a domain, a rigid relation, a state variable and an action for the problem. [7 marks] 2. Give one reason that a state-variable representation might be preferable to a representation aimed at encoding to a satisfiability problem. [1 marks] 3. Explain, giving a specific example for this problem, how the action taken at some time t can be encoded as part of a constraint satisfaction problem. [3 marks] 4. Explain, giving a specific example for this problem, how a state-variable can be encoded as part of a constraint satisfaction problem. [4 marks] 5. Explain, giving a specific example for this problem, how a precondition for an action can be encoded as part of a constraint satisfaction problem. [5 marks] 2011, Paper 7, question 2: Consider the following propositional planning problem. Start state: A, B, C, D. Goal: A, B, C, D. Actions: Action 1 has preconditions A,B,C and effect D. Action 2 has preconditions A, B and effects A and B. Action 3 has preconditions B, C and effects B and C. Action 4 has precondition B and effect B. 1. Using an entire sheet of paper, draw the planning graph as far as state level S3, where the start state is at state level S0 and the first action level is A0. Do not add any mutex links at this point. [5 marks] 2. Describe each of the five kinds of mutex link that can be incorporated in a planning graph. Add one example of each to the graph you produced in part 1. Clearly label the links to make clear which type they are. [10 marks] 3. At which level in the planning graph will all goals first be present simultaneously? Will theGraphPlan algorithm be able to extract a working plan without extending it beyond this level? Explain your answer, adding further mutex links to your diagram if necessary. [5 marks]

Evil Robot has almost completed his Evil Plan for the total destruction of the human race. He has two nasty chemicals, which he has imaginatively called A and B and which are currently stored in containers 1 and 2 respectively. All he has to do now is mix them together in container 3. His designer, an equally evil computer scientist, has equipped Evil Robot with a propositional planning system that allows him to reason about the locations of particular things and about moving a thing from one place to another. 1. Explain how this problem might be represented within a propositional planning system. Givespecific examples of the way in which the start state and goal can be represented. [5 marks] 2. Describe in detail an algorithm that can be used to find a plan using this form of representation.[5 marks] 3. Give a specific example of a successor-state axiom using the representation you suggested inpart (1). [2 marks] 4. Explain why in this particular planning problem it might be necessary to include one or moreprecondition axioms and give an example of such an axiom using your representation. [2 marks] 5. Explain why in this particular planning problem it might be necessary to include one or more action exclusion axioms and give an example of such an axiom using your representation. Suggest why it might be unwise to include too many axioms of this type, and explain how a reasonable collection of such axioms might be chosen in a systematic way. [4 marks] 6. Explain how in this problem it might be possible to include state constraints as an alternative to action exclusion axioms, and give a specific example of such a constraint using your representation. [2 marks] 2008, Paper 7, question 6: We have a simple, propositionalised planning problem and we suspect that we might be able to solve it using the GraphPlan algorithm. The problem is as follows. Action A has preconditions {X} and effects {X,Z}, action B has preconditions {Y } and effects {X,Y }, and action C has preconditions {X,Y,Z} and effects {W}. The start state for the problem is {W,X,Y,Z} and the goal is {W}.

1. Labelling the start state as level S0 and the first action level as A0, draw the planning graph for this problem up to and including level S2. Use an entire sheet of paper for this diagram. [5 marks] 2. Describe each of the five kinds of mutex link that can appear in a planning graph, and add an example of each to the diagram drawn in part (a), clearly labelling it to show which kind of mutex link it is. [10 marks] 3. What is the level cost of a literal in a planning graph? Explain why this measure of cost might perform poorly as a measure of how hard the literal is to achieve, and suggest a way in which its performance might be improved. [2 marks] 4. Will GraphPlan be able to extract a working plan from the diagram you have drawn in parts (a) and (b)? Explain your answer. You may if you wish add further mutex links to your diagram at this stage. [3 marks] 3 Bayesian Networks

Consider the following Bayesian Network:

The associated probability distributions for the binary random variables A, B, C and D are Pr(a) = 0.7, Pr(a) = 0.3 and: A Pr(b|A) B Pr(c|B) B C Pr(d|B,C) 0.1 0.2 0.6 0.15 0.95 0.5 0.4 0.3 1. Write down an expression for the full joint distribution of the random variables A, B, C and D. Compute the probability that A and B are while C and D are . [2 marks] 2. Use the variable elimination algorithm to compute the probability distribution of B conditional on the evidence that D = . [16 marks] 3. Explain why the variable elimination might not be an effective algorithm to use in practice andsuggest an alternative that addresses the shortcoming you have given. [2 marks]

Consider the following Bayesian network:

The associated probability distributions for the binary random variables A, B, C and D are Pr(a) = 0.1, Pr(a) = 0.9, Pr(b) = 0.8, Pr(b) = 0.2, and: A B Pr(c|A,B) B C Pr(d|B,C) 0.5 0.2 0.6 0.9 0.8 0.8 0.7 0.1 1. Explain why the representation of the joint distribution of A, B, C and D using the Bayesian network is preferable to a direct tabular representation. [2 marks] 2. Use the variable elimination algorithm to compute the probability distribution of B conditional on the evidence that D = . [16 marks] 3. Comment on the computational complexity of the variable elimination algorithm. [2 marks] 2005, paper 8, question 2: 1. A given probabilistic inference problem involves a query random variable (RV) Q, evidence RVs E = (E1,...,En) and unobserved RVs U = (U1,...,Um). Assuming that RVs are discrete, state the equation allowing the inference Pr(Q|E = (e1,...,en)) to be computed using the full joint distribution of the RVs and explain why in practice such a method might fail. [5 marks] 2. Give a general definition of a Bayesian network (BN), and explain how a BN represents a joint probability distribution. [4 marks] 3. Define conditional independence and explain how BNs make use of this concept to reduce the effect of the difficulties mentioned in your answer to (a). Describe the way in which conditional independence is employed by the naive Bayes algorithm. [6 marks] 4. Describe two further issues relevant to the application of BNs in a practical context and describebriefly how these issues can be addressed. [5 marks] 4 Value of Perfect Information

1. Denoting the utility of a state s of the world by U(s), and given that we have evidence E regarding the world and probability distributions Pr(s|A = a,E) modelling the effect of taking specific actions, define the expected utility associated with taking an action. [2 marks] 2. Evil Robot is, despite his undoubted evilness, very shy where romance is concerned. He hasfallen for a beautiful vacuum cleaner, called SN05718, and is wondering whether to ask her to accompany him for a change of oil. He rates the utility of going alone as 10 and the utility of being accompanied as +100. Being shy, he feels that if he asks her then she will accept with probability 0.1, but if he does not then there is a small chance of 0.01 that she will in fact ask him. What is the expected utility of this situation? [3 marks] 3. If in the scenario described in part 2 we discover that it is possible to obtain further evidence E in addition to E regarding the world, derive an expression for the value of perfect information associated with finding the value of E. [5 marks] 4. Evil Robot has a plan to acquire SN05718's diary to find out whether or not she likes him. Hebelieves that the likelihood of her liking him is 0.3. Also, if she likes him and he asks her to accompany him he thinks she will accept with probability 0.7, whereas if he does not ask she will in any case accompany him with probability 0.4. On the other hand, if she does not like him then the corresponding probabilities are 0.05 and 0.01. It will cost Evil Robot 15 to get someone to steal the diary. Compute whether or not he should. [10 marks]

1. Give a definition of expected utility and explain why the concept is useful in the context of decision-making. [2 marks] 2. Give a definition of the value of perfect information and explain why the concept is useful in the context of decision-making. [4 marks] 3. A talented, but nervous, student has to sit a difficult and important examination. There are onlytwo possible outcomes: pass or fail and the student attaches to these utilities of U(pass) = 106 and U(fail) = 108. Lacking in confidence, his beliefs are that Pr(pass|revise) = 0.55 and Pr(pass|revise) = 0.2. Calculate the expected utility of the situation described. [4 marks] 4. The student finds what he believes might be a copy of this years examination paper, discardedby a careless examiner. He believes that Pr(pass|revise,thisYearsPaper) = 0.75 However, should he be wrong then Pr(pass|revise,thisYearsPaper) = 0.1 as he will waste time learning to answer the wrong questions, because he will revise from the wrong paper. Not revising implies Pr(pass|revise,thisYearsPaper) = 0.7 However, should he be wrong then Pr(pass|revise,thisYearsPaper) = 0.08 He considers bribing somebody to tell him whether he has this years paper or not; however, he thinks it is unlikely that he in fact has this years paper, and therefore believes that Pr(thisYearsPaper) = 0.7 Compute the value of perfect information associated with finding out whether the paper is the right one. [10 marks]

An agent can exist in a state s S and can move between states by performing actions, the outcome of which might be uncertain. 1. Explain what is meant by a Utility Function within this context. [2 marks] 2. Give a definition of Maximum Expected Utility and describe the way in which it can be used to decide which action to perform next. [3 marks] 3. What difficulties might you expect to have to overcome in practice in order to implement sucha scheme? [3 marks] 4. Explain why it makes sense to use a utility function in the design of an agent, even though itcan be argued that real agents (such as humans) appear not to do this, but rather to act on the basis of preferences. [4 marks] 5. As well as actions allowing an agent to move between states, an agent might be capable ofperforming actions that allow it to discover more about its environment. Give a full derivation of the Value of Perfect Information, and explain how this idea can be used as the basis for an agent that can gather further information in a way that takes account of the potential cost of performing such actions. [8 marks calculation or program code just, then, at that point, most extreme 3 imprints [5] (ii) remark on which student(s)' name will be yield for example The main understudy with the most noteworthy score will be yield [1] 4 UCLES 2019 0478/02/SM/20 Section B 2 (a) 1 imprint for worth of c and message 51020: worth of c: 5 message: PIN OK (1 imprint) 5120: worth of c: 4 message: mistake in PIN entered (1 imprint) [2] (b) length check [1] 3 Engine Count Number Size Average OUTPUT 0 0 0 1.8 1.8 1 1 2.0 3.8 2 2 1.0 4.8 3 1.3 6.1 4 1.0 7.1 5 2.5 9.6 3 6 2.0 11.6 4 7 1.3 12.9 8 1.8 14.7 5 9 1.3 16.0 10 - 1 1.6 1.6, 5 (1 imprint) (1 imprint) (1 imprint) (1 imprint) (1 imprint) (1 imprint) [6] 4 1 imprint for every blunder recognized + recommended revision line 5: this ought to peruse IF x > h THEN h = x line 7: PRINT h ought to come after the finish of the recurrent circle line 8: this ought to peruse UNTIL c = 20 or UNTIL c >= 20 or UNTIL c > 19 [3] 5 UCLES 2019 0478/02/SM/20 [Turn north of 5 (a) 5 [1] (b) Field: At Risk Age in Years Type Map Position Table: TREES TREES Sort: Show: 9 9 Criteria: True >100 or: One imprint for each right segment. [4] 6 (a) stamping focuses: the method for finding and print the biggest worth a 1 imprint the method for finding and print the biggest worth b 1 imprint the method for finding and print the biggest worth c 1 imprint test calculation: INPUT a, b, c IF a > b AND a > c THEN PRINT a (1 imprint) ELSE IF b > c THEN PRINT b (1 imprint) ELSE PRINT c (1 imprint) [3] (b) checking focuses: circle build 1 imprint check in the event that number is a whole number 1 imprint counting the quantity of whole numbers input 1 imprint yield count esteem (outside the circle) 1 imprint test calculation: FOR x 1 TO 1000 (1 imprint) INPUT Number Difference INT(nucalculation or program code just, then, at that point, most extreme 3 imprints [5] (ii) remark on which student(s)' name will be yield for example The main understudy with the most noteworthy score will be yield [1] 4 UCLES 2019 0478/02/SM/20 Section B 2 (a) 1 imprint for worth of c and message 51020: worth of c: 5 message: PIN OK (1 imprint) 5120: worth of c: 4 message: mistake in PIN entered (1 imprint) [2] (b) length check [1] 3 Engine Count Number Size Average OUTPUT 0 0 0 1.8 1.8 1 1 2.0 3.8 2 2 1.0 4.8 3 1.3 6.1 4 1.0 7.1 5 2.5 9.6 3 6 2.0 11.6 4 7 1.3 12.9 8 1.8 14.7 5 9 1.3 16.0 10 - 1 1.6 1.6, 5 (1 imprint) (1 imprint) (1 imprint) (1 imprint) (1 imprint) (1 imprint) [6] 4 1 imprint for every blunder recognized + recommended revision line 5: this ought to peruse IF x > h THEN h = x line 7: PRINT h ought to come after the finish of the recurrent circle line 8: this ought to peruse UNTIL c = 20 or UNTIL c >= 20 or UNTIL c > 19 [3] 5 UCLES 2019 0478/02/SM/20 [Turn north of 5 (a) 5 [1] (b) Field: At Risk Age in Years Type Map Position Table: TREES TREES Sort: Show: 9 9 Criteria: True >100 or: One imprint for each right segment. [4] 6 (a) stamping focuses: the method for finding and print the biggest worth a 1 imprint the method for finding and print the biggest worth b 1 imprint the method for finding and print the biggest worth c 1 imprint test calculation: INPUT a, b, c IF a > b AND a > c THEN PRINT a (1 imprint) ELSE IF b > c THEN PRINT b (1 imprint) ELSE PRINT c (1 imprint) [3] (b) checking focuses: circle build 1 imprint check in the event that number is a whole number 1 imprint counting the quantity of whole numbers input 1 imprint yield count esteem (outside the circle) 1 imprint test calculation: FOR x 1 TO 1000 (1 imprint) INPUT Number Difference INT(number) - Number (1 imprint) IF Difference = 0 THEN Total Total + 1 (1 imprint) NEXT x PRINT absolute (1 imprint) (NOTE: option in contrast to lines 3 and 4: IF INT(Number) = Number THEN Total Total + 1 (2 imprints) ) [4] (c) Description of any two arrangements of test information. Many right responses, these are models as it were. 1000 entire numbers to guarantee that circle works appropriately 900 entire numbers and 100 numbers with decimal spots to guarantee that the routine recognizes accurately [2] 6 UCLES 2019 0478/02/SM/20 7 (a) 7 [1] (b) Hg, Cs [2] (c) Element image For this free learning action we'll begin with a little activity of class that can be utilized to address the condition of a light switch (Off or On). The class will can check whether the light is at present off or on and to flip the l.RK mber) - Number (1 imprint) IF Difference = 0 THEN Total Total + 1 (1 imprint) NEXT x PRINT absolute (1 imprint) (NOTE: option in contrast to lines 3 and 4: IF INT(Number) = Number THEN Total Total + 1 (2 imprints) ) [4] (c) Description of any two arrangements of test information. Many right responses, these are models as it were. 1000 entire numbers to guarantee that circle works appropriately 900 entire numbers and 100 numbers with decimal spots to guarantee that the routine recognizes accurately [2] 6 UCLES 2019 0478/02/SM/20 7 (a) 7 [1] (b) Hg, Cs [2] (c) Element image For this free learning action we'll begin with a little activity of class that can be utilized to address the condition of a light switch (Off or On). The class will can check whether the light is at present off or on and to flip the l.RK