2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Started: Nov 16 at 12:02
Quiz Instructions
Copyright By PowCoder代写 加微信 powcoder
The University of Melbourne
School of Computing and Information Systems
Final Exam, Semester 2, 2021 COMP90054 AI Planning for Autonomy
Duration: 150 minutes
Please note that you are permitted to write answers immediately, during the reading time, as this is
not enforced.
Instructions to Students:
The test includes questions worth a total of 40 marks, making up 40% of the total assessment for the subject.
This exam includes a combination of short-answer, long-answer, multiple-choice, and fill-in-the- blank questions. Please answer all questions in the fields provided.
This is a timed quiz. The time remaining is shown in the quiz window and will continue to count down even if you leave the Canvas site.
Open this quiz in only one browser window at a time. Opening the same Canvas quiz in multiple browser windows may cause problems with the auto-save features and some answers may be overwritten or lost.
At the end of the time limit, your answers will be submitted automatically.
Authorised Materials: This exam is open-book. While undertaking this assessment you are permitted to:
make use of textbooks and lecture slides (including electronic versions) and lecture recordings make use of your own personal notes and material provided as part of tutorials and practicals in this subject
make use of code that has been provided as part of this subject, or that you have written yourself use calculators, code, or mathematical software to compute numeric answers
While you are undertaking this assessment you must not:
make use of any messaging or communications technology
make use of any world-wide web or internet-based resources such as Wikipedia, Stack Overflow,
or Google and other search services
Typesetting math: 100%
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 1/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
act in any manner that could be regarded as providing assistance to another student who is undertaking this assessment, or will in the future be undertaking this assessment.
The work you submit must be based on your own knowledge and skills, without assistance from any other person.
Technical support
This exam is a Canvas Quiz. Technical support for this exam can be accessed at:
https://students.unimelb.edu.au/your-course/manage-your-course/exams-assessments-and- results/exams/technical-support (https://students.unimelb.edu.au/your-course/manage-your- course/exams-assessments-and-results/exams/technical-support)
Additional information about Canvas Quizzes, including troubleshooting tips, can be found here (https://students.unimelb.edu.au/your-course/manage-your-course/exams-assessments-and- results/exams/exam-types) (scroll down to the Canvas Quiz section).
Academic Integrity Declaration
By commencing and/or submitting this assessment I agree that I have read and understood the
University¡¯s policy on academic integrity. (https://academicintegrity.unimelb.edu.au/#online- exams)
I also agree that:
1. Unless paragraph 2 applies, the work I submit will be original and solely my own work (cheating);
2. I will not seek or receive any assistance from any other person (collusion) except where the work
is for a designated collaborative task, in which case the individual contributions will be indicated;
3. I will not use any sources without proper acknowledgment or referencing (plagiarism).
4. Where the work I submit is a computer program or code, I will ensure that:
a. any code I have copied is clearly noted by identifying the source of that code at the start of the program or in a header file or, that comments inline identify the start and end of the copied code; and
b. any modifications to code sourced from elsewhere will be commented upon to show the nature of the modification.
Troubleshooting
In case you cannot upload your files as requested (due to technical difficulties), please follow the steps below:
1. Name your file with your Question number followed by your Name and Student ID e.g. for Q7 for with Student ID 123456 you would upload file: Q7 123456.jpg
2. Upload your files by opening the OneDrive link below – clicking this link will open a new Tab in your
Typesetting math: 100%
browserand will prompt you to select your files for upload: https://unimelbcloud-
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 2/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
my.sharepoint.com/:f:/g/personal/adrianrp_unimelb_edu_au/Ehw9ar9QNVtOlN7qlIHLO_oBBEy
(https://unimelbcloud- my.sharepoint.com/:f:/g/personal/adrianrp_unimelb_edu_au/Ehw9ar9QNVtOlN7qlIHLO_oBBEyIbGxwxGl
Late file upload policy: For timed exams, a deduction of 1 mark from the final mark (not exam mark) for each minute late up to 30 minutes. The time stamp on the server will be used as the submission time.
Question 1 1 pts
We wish to use the A* algorithm to traverse the search tree below. Assume a fixed cost of 1 to transition between nodes and assume that ties are broken alphabetically, i.e. If node f(M) = f(N) then M will be expanded first. The first node to be expanded will be the initial node I. Which will be the parent of the 6th node expanded?
Typesetting math: 100%
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 3/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Question 2 3 pts
Assume a cost of 1 to move between nodes, initial state I, and Goal state G. With reference to the diagram above, you cannot change the heuristic values, but you can add or remove edges and add nodes with an associated heuristic value of your choice. Which nodes (with their h value) and edges do you need to add so the heuristic becomes:
(ii) Admissible (iii) Consistent (iv) Goal aware
For each property, explain which nodes/edges you need to add to the graph. If you add new nodes/edges, explain why they are needed. If some property is unachievable, explain why.
Edit View Insert Format Tools Table
Typesetting math: 100%
12pt Paragraph
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 4/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Classical Planning
Question 3 1 pts
Which of the following statements is false?
All consistent, goal aware heuristics are admissible
Depth first search is complete for acyclic state spaces
The IDA* algorithm is bounded suboptimal for admissible heuristics The hadd heuristic is inadmissible in general
The hmax heuristic is always admissible
TypesettQingumeastht:i1o0n0%4
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 5/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Below is the Bellman-Ford Table of hadd(I) for a particular problem where I is the initial state of the problem.
completed(A) completed(B) completed(C) completed(D) completed(E)
3 Infinity Infinity 1 Infinity 31
All actions have cost=1. The following actions are available Action One:
– Precondition: completed(A)
– Add: completed(B)
Action Two:
– Prec: completed(B), Completed(D)
– Add: completed(C) Action Three:
– Prec: completed(C), Completed(B) – Add: completed(E)
Action Four:
– Prec: completed(C), Completed(D) – Add: completed(E)
Compute the values of the next row, given the actions above. Update first the value of Completed(B), then Completed(C), and finally Completed(E), in that order. If you change the value of predicate Completed(B), then you can use this value in the computation of the next predicates: Completed(C) and Completed(E).
Question 5 1 pts
Below is the Bellman-Ford Table of hmax(I) for a particular problem where I = Typesett{icngomaptlhe:t1e0d0(%A), completed(D)} is the initial state of the problem.
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 6/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
completed(A) completed(B) completed(C) completed(D) completed(E)
3 Infinity Infinity 0 Infinity 30
All actions have cost=1. The following actions are available Action One:
– Precondition: completed(A)
– Add: completed(B)
Action Two:
– Prec: completed(B), Completed(D)
– Add: completed(C) Action Three:
– Prec: completed(C), Completed(B) – Add: completed(E)
Action Four:
– Prec: completed(C), Completed(D) – Add: completed(E)
Compute the values of the next row, given the actions above. Update first the value of Completed(B), then Completed(C), and finally Completed(E), in that order. If you change the value of predicate Completed(B), then you can use this value in the computation of the next predicates: Completed(C) and Completed(E).
Question 6 3 pts
Draw or define a graph such that IW(1) is guaranteed to terminate without expanding the goal.
Write down the order in which IW(1) expands the nodes in your graph, and justify
why a node is novel or not. Typesetting math: 100%
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 7/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Note: avoid making large examples, a graph with 4 nodes should be sufficient.
Edit View Insert Format Tools Table 12pt Paragraph
Question 7 5 pts
A robot’s (R) mission is to save planet earth and make sure all carbon mines (M) are closed. The robot can move directly between mines as long as it has a snack (S) for each voyage across mines. The robot can close down a mine only if it is at the same position as the mine. Initially the mines are open, and the goal is to close all the mines.
Describe briefly in STRIPS how to model the domain described. Include a specification of the parameters of the actions, and the preconditions and postconditions of each action. Include a description of the goal state of the problem, and create 1 possible initial state where the goal is reachable, and 1 possible initial state where the goal is not reachable. Your initial states need to have 3 or more snacks and 3 or more mines. Explain clearly any assumption made.
You are allowed to use variables as arguments for the actions (action schemes),
specifying the values of the variables. Note: it is not compulsory to use PDDL Typesettsinygnmtaaxth,:a1s00lo%ng as you can convey the main ideas.
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 8/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
https://canvas.l
Edit View Insert Format Tools Table 12pt Paragraph
Question 8 5 pts
Create a STRIPS problem with at most three actions such that .
Specify your STRIPS actions using the following notation:
For example, action , would stand for action a, where p and q are the preconditions, and the effects add r and delete t.
To answer this question, show your workings by 1) creating the STRIPS problem, 2) finding the value of , 3) then the value of using the best supporter function induced by , and finally 4) the value of . You then would be able to show that
Edit View Insert Format Tools Table 12pt Paragraph
Typesetting math: 100%
ms.unimelb.edu.au/courses/105724/quizzes/134139/take
)G ,I( FFh ¡ )G ,I( ddah
)I(ddah xamh
)I( ffh )I(xamh
t ton ,r ¡ú q ,p : a
)G ,I( FFh ¡ )G ,I( ddah
2021/11/16 ÏÂÎç
12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
p 0 words
Markov Decision Proceses (MDPs)
Question 9 1 pts
Consider a policy that takes a state and action, and returns the probability that action a should be chosen state s.
What type of policy is this?
A random policy
A strong policy
A deterministic policy A local policy
A stochastic policy
Question 10 1 pts
Typesetting math: 100%
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 10/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Question 11 2 pts
Match the techniques below with their properties. Multiple techniques can match to one property.
Policy iteration
Monte- Search
Q-learning
[ Choose ]
[ Choose ]
[ Choose ]
Reinforcement Learning (RL)
What is the correct formula for policy extraction of a stochastic policy if we use policy iteration? Select all correct answers.
There is no policy extraction because we learn a policy directly
Question 12 1 pts
Typesetting math: 100%
What is the difference between on-policy and off-policy learning?
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 11/16
)a ,s(¦Ð ])¡äs( V¦Ã + )¡äs ,a ,s(r[)s|¡äs(aP S¡Ês¡Æ )s(A ¡Ê a xamgra
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Question 13 1 pts
Backward induction and multi-agent MCTS are both techniques for solving extensive form games. Under which circumstances would you choose to use backward induction instead of multi-agent MCTS?
If an optimal solution is needed
If the environment is not one of the players
If there are only two players
If the game tree is small enough to solve the problem exhaustively
On-policy learning uses its policy to do exploration, while off-policy does exploration instead of exploitation
On-policy learning does temporal-difference updates based on the best possible next action executed, while off-policy does updates assuming the actual next action
On-policy learning using its policy to do exploitation, while off-policy does exploitation instead of exploration
On-policy learning does temporal-difference updates based on the actual next action executed, while off-policy does updates assuming the best possible next action
On-policy updated based on the next state, while off policy feeds back on the current state
On-policy updated based on the current state, while off policy feeds back on the next state
Question 14 1 pts
What is the difference between reward shaping and Q-function initialisation?
In reward shaping the potential function is used in the update, while in Q-value initialisation, the potential is calculated in the initial step
Nothing — they are equivalent
Reward shaping uses potential functions while Q-function initialisation uses real functions
Typesetting math: 100%
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 12/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Question 15 4 pts
Consider a reinforcement learning agent this is try to learn how fast a vacuum cleaning robot can travel without over-heating.
There are two states: cool and warm. There are two actions: slow and fast.
If the robot goes fast, it is more likely to transition to a warm state than it is goes slow.
Using a learning rate of 0.6 and a discount factor of 0.8, we arrive at the following Q-table:
Q(cool, fast) 12 Q(cool, slow) 7
Q(warm, fast) 4
Q(warm, slow)
The agent executes the action fast in the state cool, receives a reward of 6, and is now in the warm state. It will execute the action slow next.
Calculate the new value for Q(cool, fast) using 1-step SARSA to 2 decimal places.
Game Theory
Reward functions work for any Q-function representation, while for Q-function initialisation it must be a Q-table representation
Typesetting math: 100%
Question 16 2 pts
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 13/16
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
https://canvas.l
Consider the following two-player game in normal form. Select all pure strategy Nash equilibrium for this game, if any exist.
A, D: (0, 0) B, D: (15, 25) C, D: (10, 5) A, E: (0, 0) B, E: (25, 25) C, E: (15, 5) A, F: (5, 10) B, F: (5, 15) C, F: (10, 10)
Question 17 2 pts
In your own words, compare the concepts of pure strategy and mixed strategy in normal form games.
Edit View Insert Format Tools Table 12pt Paragraph
Typesetting math: 100%
ms.unimelb.edu.au/courses/105724/quizzes/134139/take
2021/11/16 ÏÂ
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 15/16
12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
p 0 words
Question 18 5 pts
In a family, there are three siblings: Alice, Bob, and Caroline. They back a cake together, which weighs one kilogram. Alice is given the task of cutting the cake into three pieces, which have the sizes s1, s2, and s3, and where the three pieces of cake can be different sizes.
Bob gets to choose the first piece of cake, then Caroline. Alice chooses last. The payoff is the size of the cake.
Assuming that s1 <= s2 <= s3, using game theory, show that the best thing Alice can do to maximise her utility is divide the cake into three equal pieces. Show your working.
Typesetting math: 100%
2021/11/16 ÏÂÎç12:03 Quiz: Exam: AI Planning for Autonomy (COMP90054_2021_SM2)
Not saved Submit Quiz
Typesetting math: 100%
https://canvas.lms.unimelb.edu.au/courses/105724/quizzes/134139/take 16/16
程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com