Introduction to Artificial Intelligence Assignment 1

CS 486/686: Introduction to Artificial Intelligence Assignment 1
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Submission Instructions:
• Assignments are to be submitted through LEARN, in the Dropbox labelled Assignment 1
Submissions in the Assignment 1 folder.
• Late assignments will be accepted up until October 4th 23:59. Please read the course policy
on assignments submitted after the official due date. No assignment will be accepted, for
any reason after Friday, October 4 at 23:59.
• The following exercises are to be done individually.
• For the programming questions you may use the language of your choice. That said, we
highly recommend Python.
• You should submit TWO files for the assignment.
– writeup.pdf
∗ Include your name, your uwaterloo email address, and your student number.
∗ If you handwrite your answers, make sure your handwriting is legible and the pictures or scan is clear. You may get a mark of 0 if we can not read your handwriting.
– code.zip
∗ Include your programs, scripts to run your programs, and README.txt file with
instructions to run the script. You may get a mark of 0 if we cannot run your
program. Ensure that your code is well documented. You may lose points if we
can not understand your documentation.
Lead TA: Ehsan Ganjidoost (eganjido@uwaterloo.ca)
Question 1 ( 50 points)
In this question you are asked to implement a search algorithm for solving the Traveling Salesperson Problem (TSP). In a TSP a salesperson must visit n cities, visiting every city exactly once and
returning to the city they started from. The goal is to find the tour with the lowest cost. Assume
that the cost of traveling from one city to the next is the Euclidean distance between the two cities.
That is, given two cities with coordinates (x1, y1) and (x2, y2), the cost of traveling between the
two is
q
(x1 − x2)
2 + (y1 − y2)
2
.
The data can be found in the file TSPproblem.zip which is located in the Assignment 1 folder.
The data format of the problem instances is as follows;
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CS 486/686: Introduction to Artificial Intelligence Assignment 1
< number of cities
< city id < X < Y
< city id < X < Y
.
.
.
.
.
.
.
.
.
< city id < X < Y
The < city id is a capital letter. The identifiers < X and < Y represent the x and y
coordinates (integers) of the city. Each city is on its own line and the number of cities is on the
first line by itself.
Example:
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A 17 55
B 24 38
C 91 57
Write a program that uses the A∗
algorithm to solve the Traveling Salesperson Problem (TSP).
Let the cost to move between cities be the Euclidean distance. In the code, try to separate the
domain independent part (search algorithm) from the domain specific part (TSP related functions).
a. Give a representation of the problem suitable for A∗
search. That is, describe states, their
representation, the initial state, the goal state, the operators, and their cost.
Use the following conventions;
• Always start from node A. (You might as well since every tour has to go through A; thus A
never needs to be backtracked.)
• Generate the successors of any given node in alphabetical order.
• Count nodes as they are generated as successors.
Generate and use a good admissible h function, and describe it in detail in the write-up. Think
carefully about how to construct the h function since some choices of h functions may be too poor
to allow you to execute the search in practice. If your program spends more than 5 minutes on a
problem instance of 10 cities, something is going horribly wrong: terminate the run and rethink
your design. (On the other hand, don’t panic if you can solve the 10-city instances quickly. That
likely means your heuristic and implementation is fine.)
b. Describe your heuristic function.
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CS 486/686: Introduction to Artificial Intelligence Assignment 1
c. Run A∗
search with your heuristic function on the provided random test instances. Note that
there are 10 instances for each number of cities. Determine how many nodes, on average, A∗
generates for each number of cities, and plot them. Extrapolate from these results roughly
how many nodes A∗
search would generate for a 36 city problem. To do this, you may want
to use a logarithmic scale on the y-axis. How long would such a search take? (If you want,
you can try to run your implementation on the 36-city problem, but we do not require that
your implementation successfully solves this particular instance).
d. Repeat the same experiment described in part c. using the heuristic function h(n) = 0 for all n.
If the search takes more than 5 minutes (or you run out of memory) on any of the runs then
you can stop early and record that it did not terminate.
Submit the following
• A well documented copy of your code.
• Your problem representation.
• The description of your heuristic function.
• The plot of the average number of nodes generated for each number of cities for both your
heuristic function and the heuristic function h(n) = 0. You can do this using two different
plots, or by plotting everything on the same graph. Just make sure that everything is well
labelled.
• A discussion on the difference in performance when using the two different heuristic functions.
• A discussion of how you expect A∗
search to perform on a 36-city problem instance for both
heuristic functions.
Mark Breakdown: 10 pts for problem description, 10 pts for a “good” heuristic, 10 pts for plots,
15 pts for discussion, 5 pts for documentation and basic performance (i.e. you should be able to
solve a 16-city problem).
Question 2 [50 pts]
In the question you are asked to implement a CSP algorithm for solving Sudoku puzzles. Sudoku
is a simple game of deduction, usually played on a 9x9 grid. In the goal state, each number from 1
to 9 appears exactly once in each row and column on the grid. Additionally, the grid is subdivided
into 9 3x3 non-overlapping subgrids, and each number must appear exactly once in each subgrid. A
Sudoku puzzle usually starts with some numbers filled in, so that there is a single unique solution.
An example of such a puzzle is given below:
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CS 486/686: Introduction to Artificial Intelligence Assignment 1
a. Write a formal description of Sudoku as a CSP, giving a list of variables, their domains, and the
constraints between them.
b. Implement a CSP solver for Sudoku. In particular your solver should have three versions:
Version A : Standard backtracking search
Version B : Standard backtracking search + forward checking
Version C : Standard backtracking search + forward checking + heuristics (most restricted
variable, most constraining variable (for tie breaking) and least constraining value).
Have your solver (for each version) count the total number of variable assignments it makes
(including when it backtracks).
c. You can find a set of Sudoku problems with different numbers of initial values in the SudokuProblem.zip file included in the Assignment 1 folder on Learn. The puzzles are organized so that all puzzles in a particular sub-directory have the same number of initial values
(corresponding to the name of the sub-directory).
Each file contains 9 rows of exactly 9 numbers. Numbers within a line are separated by
whitespace. The value 0 is used to indicate a blank space in the grid. For example, the first
line of the example puzzle above would be written:
5 3 0 0 7 0 0 0 0
For each version of your solver (Version A, Version B and Version C), run it on each problem
instance, counting the number of variable assignments made on each instance. To avoid
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CS 486/686: Introduction to Artificial Intelligence Assignment 1
spending a (very) long time collecting data, your solver should ‘give up’ if it needs to take
more than 10,000 steps.
For each version of your solver, create a plot of your findings, where on the x-axis you have
the number of initial values, and the y axis is the average number of variable assignments
for each initial value. You can choose to either produce three different plots (one for each
version of your solver) or include the three plots in a single graph. Just make sure that
everything is well labelled.
Describe your findings and provide an explanation for what you observe.
Submit the following
• A well documented copy of your code
• A written description of your problem representation
• The plot of the average number of variable assignments against the number of initial values
the sample Sudoku problems
• A discussion of your findings and an explanation for why your program behaves as it does.
Mark Breakdown: 5 pts for CSP definition, 5 pts for documentation and presentation of results,
20 pts for algorithm behaviour, 20 pts for discussion
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