OFFICE OF ACADEMIC AFFAIRS
Reference No. : XMUM.OAA – 100/2/8-V2.0 Effective Date : 23 APRIL 2018
DESCRIPTION OF COURSEWORK
Course Code
Copyright By PowCoder代写 加微信 powcoder
Course Name
Data Management and Artificial Intelligence
Dr Kumaran
Academic Session
Assessment Title
Assignment 3
A. Introduction/ Situation/ Background Information
This assignment is to evaluate the ability of students in applying the basic programming principles.
B. CourseLearningOutcomes(CLO)covered
At the end of this assessment, students are able to:
CLO 3 Demonstrate the basic principles of Artificial Intelligence
C. University Policy on Academic Misconduct
1. Academic misconduct is a serious offense in Xiamen University Malaysia. It can be defined as any of the following:
i. Plagiarism is submitting or presenting someone else’s work, words, ideas, data or
information as your own intentionally or unintentionally. This includes incorporating published and unpublished material, whether in manuscript, printed or electronic form into your work without acknowledging the source (the person and the work).
ii. Collusion is two or more people collaborating on a piece of work (in part or whole) which is intended to be wholly individual and passed it off as own individual work.
iii. Cheating is an act of dishonesty or fraud in order to gain an unfair advantage in an assessment. This includes using or attempting to use, or assisting another to use materials that are prohibited or inappropriate, commissioning work from a third party, falsifying data, or breaching any examination rules.
2. All the assessments submitted must be the outcome of the student. Any form of academic misconduct is a serious offense which will be penalised by being given a zero mark for the entire assessment in question or part of the assessment in question. If there is more than one guilty party as in the case of collusion, both you and your collusion partner(s) will be subjected to the same penalty.
D. Instruction to Students
You are required to answer all the assignment questions in Section F. This is an individual assignment. You are required to prepare one softcopy of a typewritten report detailing the answers to the assignment questions. The deadline for this assignment is 11th March 2021. Late submission will result in a penalty of 0 marks.
E. EvaluationBreakdown
Component Title
Percentage (%)
Breadth First Search, Depth First Search and Iterative Deepening Search
Breadth First Search and Depth First Search
Hill Climbing and Simulated Annealing
Machine Learning
F. Task(s)
1. [CLO3] Draw your own state space diagram, where, the start state begin with value 5 and
following successors’ state n will be return two states, where with the following rule of 5n and 5n+1. [Use Figure 1, as your example which shows n and n+1 tree].
Figure 1: Sample n and n+1 till Level 3
(a) Makesureyourstatespacediagramforstatesbeginfromvalue5andendsatvalue656.
(b) Suppose the goal state is 651. List the order of states that will be visited for the following search space algorithms. Remember to STOP when you reach the goal state.
(i) Breadth-first search [2 marks]
(ii) Depth-first search [2 marks]
(iii) Iterative deepening search [4 marks]
(c) Figure 2 illustrates the search space, where,
S indicates as the START node
G1 and G2 are the GOAL nodes
*There are two GOAL nodes*
Labeled arcs consist of cost of traversing the arcs (COST FUNCTIONS)
Each node is the value to estimate of the distance of the goal (HEURISTIC
FUNCTIONS)
Please assume that the mentioned search algorithms are COMPULSORY to choose the LEFT BRANCH FIRST whenever there is an option.
Figure 2: Searching Space
Provide your answers for Depth-First, Breadth-First and Iterative-Deepening. For each of the
following search strategies,
(a) Indicate which goal state is reached first and second; (b) List the states in order till the reached goal
Depth-first Search [4 marks]
Show the goal states that is reached first and second, also the depth-first search sequence:
Breadth-first Search [4 marks]
Show the goal states that is reached first and second, also the breadth-first search sequence:
Iterative Deepening Search [4 marks]
Show the goal states that is reached first and second, also the iterative deepening search sequence:
2. [CLO3] Figure 3 illustrates the initial state of 8-puzzle problem,
(a) You should use breadth first search (BFS) and illustrate the state of the search tree that can be built down to level/depth 2 (Assume: Level/Depth 0 is the root of the tree). [5 marks]
(b) You should use depth first search (DFS) and illustrate the state of the search tree that can be built down to level/depth 3 (Assume: Level/Depth 0 is the root of the tree). *Remember to STOP once you successfully expanded one node that reaches to level/depth 3 * [5 marks]
3. [CLO3] In order to test your understanding on simulated annealing algorithm, show the simulated annealing algorithm’s representation and explain the algorithm in your own words. Include your explaination with the advantages of using simulated annealing algorithm instead of hill climbing algorithm. [15 marks]
4.[CLO3] Weka is a platform for machine learning algorithms. Employ the following classifier and report their respective accuracy.. You are required to download and install WEKA: Data Mining Software in Java from https://www.cs.waikato.ac.nz/ml/weka/ in order to complete the following tasks. You also will be provided with the following data sets in order to complete all the tasks. Briefly explain each stated algorithms.
Naïve Bayes [5 marks]
Decision Trees [5 marks]
k-Nearest Neighbors (kNN) [5 marks]
Tabulate the results obtained from Weka based on the following settings with accuracy rates in following Table 1 till Table 3. [30 marks]
Finally, draw a conclusion from Table 1 till Table 3 with the obtained results. [5 marks] Table 1
Naïve Bayes
Cross-Validation (%)
Decision Trees
Cross-Validation (%)
Cross-Validation (%)
APPENDIX 1
MARKING RUBRICS
Component Title
Assignment 3
Score and Descriptors
Total marks
Question 1
[25 marks]
Draw the state space diagram with given rules.Each correct value and proper branches at every level (1 mark) and all correct values of 3n and 3n+1 (1 mark)
BFS(2 mark); DFS (2 mark); IDS (every iteration 1 mark)
State correct visited order (2 marks) and goal node (2 marks)
Question 2
[10 marks]
Each correct level (1 mark) and entire correct diagram (1 mark)
Each correct level (1 mark) and entire correct diagram(1 mark)
Question 3
[15 marks]
Representation of SA algorithm (5 marks)
Explaination of SA algorithm (5 marks)
Comparison of advantages between hill climbing and Sa algorithm (5
Question 4
[50 marks]
Discuss every algorithm based on your understanding: Naïve Bayes [5 marks]
Decision Trees [5 marks]
k-Nearest Neighbors (kNN) [5 marks]
Results for each Table 1-3 (10 marks)
Differentiate each Table results and identify the best result.
Note to students: Please attach this appendix together with the submission of coursework
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