java代写 | assignment | ARTIFICIAL INTELLIGENCE | ai代写 – CSC 520 – ARTIFICIAL INTELLIGENCE

2022-08-19

CSC 520 – ARTIFICIAL INTELLIGENCE

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assignment 2.

Due Date : March 7th, 2022

Thisassignment consists of two questionswhich involvewrittenanswers andcode. Inorderto completethe assignmentyou mustsubmitawrittenreportinpdfformdetailingyouranswerstothe questionsaswellasyourcode.Asdiscussedinclassallworkmustbeyourown.Youmaynotusethird partylibrariesorexamplecodetocompletetheassignment.Allreportsmustbeclearandwellwritten.All codemustbeclear,readable,andwell-commented.PleaseincludeaREADMEwithinstructionsonhow to run the code.

QUESTION 1 – Part (a) ( 65 Points )

ProblemDescription: Youaregivena2dsearchspaceofsize(m,n)whereeachcellholdsanumber.At eachcell,youcanmoveleft,right,upordownbythenumberofplaceslistedinthecurrentcell.Inthe exampleshownbelow,fromthecell(0,0)wecanmovetotherightby 2 cellsto(0,2)ormovedownby 2 cells to (2,0).

Implement code to reach the goal cell (m,n) using the following techniques:

  1. Steepest Ascent Hill Climbing – Consider the initial state as (0,0)
  2. Steepest Ascent Hill Climbing with Random restart
  3. Steepest Ascent Hill Climbing with Random walk – Consider the initial state as (0,0)

Implement the algorithms using both the heuristics mentioned below: ( 60 Points) Heuristic 1 (H1): Ifyouareeitherinthesameroworsamecolumnasthegoalthenconsidertheheuristic value as 1, otherwise consider the heuristic value as 2.

Heuristic 2 (H2): Use manhattan distance between thecurrent cell and goal cell as the heuristic value.

For each 2d space you are expected to calculate the following:

  1. Find the path taken to reach (m,n). If not possible then your code should return Not Possible
  2. Find the total number of nodes visited
  3. For Random Restart you are expected to print the initial state for every restart
  4. ForRandomWalkyouareexpectedtoprintthemovechosen-bestmoveorrandommoveat every step.

Note: Preference of directions : left > right > up > down Rememberdifferentparametersyoucanchooseforyoursearch(e.g.numberofstepstotake beforefinishing the search(searches 1, 2, and 3), numberofrandomrestarts(search2), probability value (search 3).

 Fornumberofstepstotakebeforefinishingthesearch,choosevaluesintherange[500-1000]
with a step value of 100
 For number of random restarts, choose values in the range [10 - 25] with a step value of 5
 For the probability value, choose values in the range [0.2 - 0.8] with a step value of 0.
 When using the random function please set the seed value as 1234

Now that you have implemented the algorithms, in the solutions PDF explain the following: ( 5 Points)

  1. Conduct exploration to find a reasonable combination of values forthese parametersand investigatethetrade-offbetweenefficiencyandaccuracy.Provideananalysisoftheexploration in the solution PDF. Explain your search strategy (this can be a simple grid search).

Sample Maze:

2 , 2 , 1 , 1

1 , 2 , 1 , 1

2 , 1 , 1 , 0

QUESTION 1 – Part (b) ( 40 Points )

Usingthe same ProblemDescription asinPart(a)andManhattanDistancefortheHeuristicvalues, implement RBFS to reach the goal state. Postimplementation,pleasenotethenumberoftimesbacktrackingtakesplaceforeachmazeanddisplay it in a table.

Note: Preference of directions : left > right > up > down

Sample Maze:

2 , 2 , 1 , 3

1 , 2 , 1 , 1

2 , 1 , 1 , 0

Heuristic Values (Manhattan Distance):

5 , 4 , 3 , 2

4 , 3 , 2 , 1

3 , 2 , 1 , 0

Please output the result in the following format at each stage:

Node Selected: [0, 0] F-Value: 5 , alternative_best_f: 9223372036854775807 Further Child Nodes: [0,2],[2,0],

———————————–

Node Selected: [0, 2] F-Value: 4 , alternative_best_f: 4 Further Child Nodes: [0,1],[0,3],[1,2],

Node Selected: [0, 3] F-Value: 4 , alternative_best_f: 4 Further Child Nodes: [0,0],

Node Selected: [1, 2] Backtracking to bestalternative takes place in this step. F-Value: 4 , alternative_best_f: 4 Further Child Nodes: [1,1],[1,3],[0,2],[2,2],

Node Selected: [1, 3] F-Value: 4 , alternative_best_f: 4 Further Child Nodes: [1,2],[0,3],[2,3],

Node Selected: [2, 3] F-Value: 4 , alternative_best_f: 4

QUESTION 1 – Part (c) ( 5 Points ):

Basedonyouroverallworking,inthesolutionPDF,determineiftheheuristicsH1andH2areadmissible and / or consistent? Explain your reasoning.

Execution Instructions :

Part 1(a):

  1. Please make sure that your code is executable on running the command : For Python: python3 q1a.py <hc / rr / rw> Example command : python3 q1a.py Maze1.txt hc
For Java:
javac q1a.java
 java q1a <maze filename> <hc / rr / rw>
  1. Writethe outputof the program toatext fileintheQ1a/Solutionsfolderwiththenaming convention – <Maze_filename>_solution.txt.
  2. AnswerstoHillClimbing,Random restart,Randomwalkmust beintheQ1a/Solutions/HC, Q1a/Solutions/RR, Q1a/Solutions/RW folders respectively.

SamplesolutionfilesforafewmazeshavebeenaddedtotheQ1a/Solutionsfolder.Thesolutionswere generated using parameter values of 1000 iterations, 10 restarts and 0.8 probability.

Part 1(b):

  1. Please make sure that your code is executable on running the command : For Python: python3 q1b.py Example command : python3 q1b.py Maze1.txt
For Java:
javac q1b.java
java q1b <maze filename>
  1. Writethe outputof the program toatext fileintheQ1b/Solutionsfolderwiththe naming convention – <Maze_filename>_solution.txt.
  2. Answers must be in the Q1b/Solutions folder respectively.

Sample solution files for a few mazes have been added to the Q1b/Solutions folder.