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COMP532 Computer Science
创建日期：2024-04-12 18:11:00
标签： COMP532
Computer Science

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COMP532 Computer Science SECOND SEMESTER EXAMINATIONS 

 Machine Learning and BioInspired Optimization INSTRUCTIONS TO CANDIDATES 1. Answer FOUR questions. If you attempt to answer more questions than the required number of questions, the marks awarded for the excess question answered will be discarded (starting with your lowest mark); 2. You should enter your answers in a word processing document, e.g., MS Word or LaTex (Overleaf); 3. Some questions require a diagram - you can either just sketch this on plain paper, and then scan/photograph, or using any drawing software (e.g., MS word, MS PowerPoint and Google Drawings), and insert into the word processing document; 4. Having completed your answers, convert to PDF and submit via VITAL before 10am, Friday 22nd May. It is strongly suggested to submit your answers at least half an hour before the deadline, i.e., 9:30am Friday 22nd May, in case you miss the deadline of submission; 5. You MUST complete the exam by the above stated ending time; 6. You can only submit ONCE; 7. Expected Writing Time: 3 hours. 8. Please do NOT include your name in your answer sheet. 9. This is an open-book assessment. Copying any material from another source, or colluding with any other person in the preparation and production of this work will be considered suspected academic misconduct and will be dealt with according to the University’s Academic Integrity Policy. 10. The use of a calculator IS allowed. PAPER CODE COMP532 page 1 of 5 Continued 1. Reinforcement Learning (25 marks) (a) (8 marks) The following table is a list of action values at time t . Calculate the action probabilities for a1, a2 and a3 according to Softmax Action Selection using the Boltzmann distribution with τ = 2. an a1 a2 a3 Qt (an) -1 4 2 (b) (10 marks) Consider the 2 × 2 deterministic grid world in Figure 1(a). Allowable moves are shown by arrows, and the numbers indicate the reward for performing each action. At time step t , a Q-learning agent has computed Q values as given in Figure 1(b); e.g., Q(LowerLeft ,Up) = 15 and Q(LowerLeft ,Right) = 20. Show the changes in the Q value estimates when the agent subsequently takes the path shown by the dotted line in Figure 1(c) (the agent starts in the lower left cell) when learning rate α = 0.5 and discount factor γ = 0.75. Clearly show how you computed your answer. Figure 1: The deterministic grid world. (c) (7 marks) Draw the backup diagram of the Temporal Difference learning method. In ad- dition, explain bootstrapping and sampling using the diagram. 2. Multi-Agent Reinforcement Learning (25 marks) (a) (15 marks) Below is the payoff matrix for the Prisoners Dilemma game. (Row player: x; Column player: y; C: cooperate; D: Defect) C D C (3, 3) (0, 5) D (5, 0) (1, 1) (1) State the Nash Equilibrium of the game and explain your answer. (2) Consider that at some time t that x = (0.3, 0.7) and y = (0.6, 0.4), where the first argument in the vectors is the probability of cooperating and the second argument is the probability of defecting for players x and y respectively. According to the replicator dynamics, calculate x˙1 and y˙1, i.e., the change in probability of playing the first action (C), for both players. Show how you computed your answer. PAPER CODE COMP532 page 2 of 5 Continued (b) (5 marks) Fig. 2 is the direction field plot of the Rock-Paper-Scissors game. According to the plot, is there a Nash equilibrium for the game? If yes, is the Nash equilibrium evolutionarily stable? Explain your answer. Figure 2: The direction field for the Rock-Paper-Scissors game. (c) (5 marks) What are Markov Games, and how do they extend repeated games and MDPs? Provide an (abstract) example. 3. Swarm Intelligence (25 marks) (a) (15 marks) Consider a Travelling Salesman problem as shown in Fig. 3: there are 4 different cities, i.e., Liverpool (A), Manchester (B), Leeds (C) and Sheffield (D), and the edges represent the connection between the cities; the numbers on the edges represent the distances between the cities; a salesman travels over the graph who aims to find a closed tour of minimal length connecting each city - using Ant System. Take ηij = 1/dij , where dij is the distance between cities i and j . Figure 3: The map of connections between Liverpool, Manchester, Leeds and Sheffield. PAPER CODE COMP532 page 3 of 5 Continued (1) For one ant starting at Liverpool, calculate the values of p1AB(1), p 1 AC(1), p 1 AD(1). Take parameter values α = 1, β = 3, ρ = 0.5,Q = 100, τ = 10−6. (2) Consider that the ant took the tour A→ B→ C→ D→ A. Calculate: τAB(2) and τAC(2). (b) (5 marks) Draw a graph to show the velocity update of the Particle Swarm Optimisation algorithm and give a brief explanation of your graph. (c) (5 marks) What type of multi-agent learning is Swarm Intelligence, and what are its main benefits? 4. Deep Learning (25 marks) (a) (6 marks) Figure 4 is a network for the function y = wx + b. At the current time, x = −3,w = 2,b = 3. Calculate the updated w by applying back-propagation. Figure 4: The network. (b) (6 marks) (1) In one convolution layer, we have 8 filters of 3x3x3 applied to input volume 64x64x3 with stride 1 and pad 1. What is the size of the output volume? Give details of how you calculate the size of the output volume. (6 marks) (2) Using this example, explain how to apply the convolution operation to the input volume to get the output volume. (c) (7 marks) Draw a graph to explain the structure of a Deep-Q network that would be suitable for an Atari Games agent. 5. Artificial Immune Systems and DNA Computing (25 marks) (a) (5 marks) Explain Clonal Selection in the Artificial Immune System. (b) (5 marks) 

What are the common characteristics of Artificial Immune Systems and Rein- forcement Learning?

 Give your explanations. (c) (5 marks) What are the five characteristics of AIS in common with Swarm Systems? 

Give your explanations. PAPER CODE COMP532 page 4 of 5 Continued (d) (5 marks) 

What are the common characteristics in the Parallel Problem Solving methods we have covered,

 including single-/multi-agent learning, deep learning, swarm intelli- gence, 

artificial immune system and DNA computing? Give your explanations. (e) (5 marks) 

What are the five steps of Adleman’s experiment in DNA computing to solve the Traveling Salesman Problem (TSP)

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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