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COMP90054 assignment
Creation date：2024-05-25 16:26:34
Belonging category：计算机computer science
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COMP90054 assignment

make them reflect on their own learning, so this is good! In addition, many people
who liked ungrading commented that it reduced the stress of trying to get every
test case right.
There were many reasons people liked ungrading, chiefly:
. Independence and autonomy: people felt more responsible for their own learning
and appreciated the additional autonomy and independence that ungrading
provided.
. Reduced the stress of trying to get everything right and figure out what the staff
wanted to see.
. Allowed people to explore and take a few more risks, because if they failed,
learning still occurred.
More generally, we saw several people either not submitting a self evaluation or not
really taking the time to reflect on their learning. In this assignment, we make the
expectations clearer: students are marked based on their self evaluation.
Collaboration
Assignment 2 can be completed individually or in pairs. We encourage pairs to work
together to learn from each other; not to simply split the tasks for efficiency. But we
will not monitor this -- it is your responsibility. You can submit different solutions or
the same solution, we just want to encourage collaboration.
For the student works in pairs, you must submit an individual short self evaluation.
We encourage students to derive their own tests for the tasks and share them with
others to help with learning.
The Domain - Bubble Tea robot
Your task is to model a robot that makes bubble tea. The owner of the company has
just a few hard-coded recipes, but would prefer that customers are able to flexibly
specify their own bubble teas.
The robot is able to do three things: (1) add ingredients to a cup; (2) mix the
ingredients that are in the cup; and (3) heat the ingredients that are in the cup.
There are four main types of ingredients: (1) tapioca balls; (2) syrup; (3) ice; and (4)
the tea itself. The model can add any of these to the cup at any point. Syrup and
tapioca balls can be flavoured.

Customers can specify whether they would like their drink mixed or not (modelling as
a PDDL predicate in a goal). If the tea and syrup are mixed, the drink is considered to
be "mixed". However, if the tapioca balls are mixed, the mixer blends them into a
syrup, so there are no longer tapioca balls in the drink.
When ice is added to the cup, the drink is considered to be iced. Otherwise, it is not
iced (modelled as a PDDL goal).
The cup can be heated in a microwave. Typically, customers like heated tapioca balls
(modelled as a PDDL goal), otherwise the tapioca balls are too hard. If the ice is
heated, it melts so the drink is no longer iced. Customers can also specify that they
would like the tea or the syrup iced as well (modelled as a PDDL goal).
The robot can make just one bubble tea at a time.
Tasks
You can use the classical propositional STRIPS subset of PDDL + typing, negative
preconditions and conditional effects. You are responsible for creating the domain
and problems files, specifying any types, predicates, and actions (including action
parameters, preconditions and effects), initial states, and goal states.
Task 1 [0 marks]
As a first task, model PDDL actions (can be one action or multiple) for adding the
ingredients into a drink. Only one of each ingredient can be added into a single drink.
For this, you will also need to model the PDDL predicates, constants, etc.
Once you have actions modelled, specify problems to test the following:
A. A customer can create an iced drink with the tea, mango tapioca balls, and
mango syrup.
B. A customer cannot create an iced drink with the tea, ice, and two different
flavours of syrup.
Task 2 [0 marks]
The second task is to model heating the cup in the microwave, and mixing the
ingredients of the cup.
Once you have actions modelled, specify problems to test the following:
A. A customer can create a mixed drink with the unheated tea, heated mango
tapioca balls, unheated mango syrup, and no ice.

B. A customer can create a mixed drink with no tapioca balls, unheated mango
syrup, unheated lime syrup, and ice. (Think carefully about this. If you have
modelled the mixing properly, this should be possible).
You may need to change your actions from Task 1 for this. That's fine -- you should
only submit the final versions.
Task 3 -- Challenge task [0 marks]
This task is a more challenging task that will take more time than the first two tasks,
but you may wish to complete it if you want to learn more about PDDL.
The owners developing the robot have noticed that if the cup is heated in the
microwave, the cup becomes too hot to handle. Fortunately, they have just upgraded
the robot so that it can use two cups at a time.
Create a new version of your model that has two cups.
It is strongly recommended that you model this in a new domain file.
You should have the same actions as before, but this time, the actions apply to one of
the cups. When heated in the microwave, the cup should be noted as being hot. In
addition, the robot can tip all ingredients from one cup into the other, between either
cup. This will tip all ingredients.
Once you have actions modelled, specify problems to test the following:
A. A customer can create a mixed drink with the unheated tea, heated mango
tapioca balls, unheated mango syrup, no ice, and an unheated cup. This is the
same as Task 2.a, but with an unheated cup.
B. A customer can create a mixed drink with no tapioca balls, unheated mango
syrup, heated lime syrup, ice, and an unheated cup. This is the same as Task 2.b,
but with an unheated cup and with heated lime syrup.
Task 4 -- Self evaluation [10 marks]
For each question, write a brief self evaluation that includes how many marks you
believe that you have earnt in ths assignment. Your self evaluation for each question
must include:
Self evaluated marks for each question, with a breakdown of:
3 marks for Task 1
5 marks for Task 2
2 marks for Task 3

A brief overview of why you have earnt this. As part of your overview, consider
the following:
whether you can successfully generate correct plans for the goals;
explain how you checked the correctness of your plans;
whether you believe that your model is robust -- that is, whether it can
generate correct plans for goals other than those specified above and why
you believe it is robust;
whether your model is clear, concise, and at the 'right' level of abstraction --
that is, the model contains only those aspects that are needed to solve the
problem;
what you learnt about modelling and abstraction; and
list any assumptions that you have made.
For each task (1-3), keep this to 2-3 paragraphs.
Note that completing the tasks without submitting a self evaluation will not attract
ANY marks. You must tell us the evidence that you use to self-evaluate yourself.
Ambiguous details?
You may have questions about the above description, such as: "what should happen if
this happens, but then that -- what should the behaviour be?".
The answer: just choose a sensible behaviour and model it. We don't care if the model
does exactly what our model does -- we care that you are learning about modelling
and abstraction. You should be able to make a sensible assumption about the
behaviour and model it. You will still learn the same amount!
Debugging tips and other resources
If the planner is failing to return a plan, but you think it should, debugging is more
difficult than in imperative programming languages, because the model is declarative
and there are no standard PDDL debuggers.
There are two techniques to help debug the model:
. Simplify the goal: If your goal contains multiple predicates, try removing them
systematically to see which goal (or combinations of goals) are preventing the
solution. This helps to identify which actions you may have modelled incorrectly.

. Use preconditions as goals: If tip 1 does not work, write out the plan that you
expect it to find. Then, create a new problem instance, take the first action of
your expected plan, and set its precondition as the goal. If it fails, you have
identified where your model is not doing what you expect. If it does not fail, move
to next action in the plan, setting its precondition as the goal, and iterate.

Submission Instructions
Your submission has three parts: a session saved on planning.domains; a PDF file
submission on Canvas; and a submission certification form.
planning.domains submission

See a video below showing how to do this.
PDF report submission
Your submission should contain a report that includes:
. Your unimelb student ID and student email address, and the name of anyone
partner that you completed the assignment with.

. The PDDL domain file(s), cut and paste into the PDF report.
. The PDDL problem files, cut and paste into the PDF report.
. The output generated by the problem files. That is, the plan if it generates a plan,
or the output specifying that no plan was found.
. Your self evaluation for each question.
. An optional appendix, if you want to add additional details, such as additional
problem files or solutions that you tried that didn't work out.
Case category
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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 EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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