Description

Hwanjo Yu
CSED342 – Artificial Intelligence
General Instructions
This (and every) assignment has a written part and a programming part.
You should write both types of answers in submission.py between
# BEGIN_YOUR_ANSWER and
# END_YOUR_ANSWER
ÒThis icon means a written answer is expected. Some of these problems are multiple choice questions that impose negative scores if the answers are incorrect. So, don’t write answers unless you are confident.
¥This icon means you should write code. you can add other helper functions outside the answer block if you want. Do not make changes to files other than submission.py.
Your code will be evaluated on two types of test cases, basic and hidden, which you can see in grader.py. Basic tests, which are fully provided to you, do not stress your code with large inputs or tricky corner cases. Hidden tests are more complex and do stress your code. The inputs of hidden tests are provided in grader.py, but the correct outputs are not. To run all the tests, type
python grader.py
This will tell you only whether you passed the basic tests. On the hidden tests, the script will alert you if your code takes too long or crashes, but does not say whether you got the correct output. You can also run a single test (e.g., 3a-0-basic) by typing
python grader.py 3a-0-basic
We strongly encourage you to read and understand the test cases, create your own test cases, and not just blindly run grader.py.

In this assignment, you will formulate some problems as constraint satisfaction problems (CSPs) which consist of variables and unary or binary factors between the variables. Once you defined CSPs, you can find the solutions by backtracking search. However, its run-time increases exponentially with the number of variables and factors. Therefore, you will also implement some heuristics which reduce the required time for backtracking.
Problem 0. Warm-up
Problem 0a [3 points] ¥
Let’s consider a CSP with n variables X1,…,Xn and n − 1 binary factors t1,…,tn−1 where Xi ∈ {0,1} and ti(X) = xi Lxi+1. Note that the CSP has a chain structure. The figure below illustrates an example of the factor graph with 3 variables.

Implement create_chain_csp() by creating a generic chain CSP with XOR as factors.
Note: We’ve provided you with a CSP implementation in util.py which supports unary and binary factors. For now, you don’t need to understand the implementation, but please read the comments and get yourself familiar with the CSP interface. For this problem, you’ll need to use CSP.add_variable() and CSP.add_binary_factor().
Problem 1: CSP solving
So far, we’ve only worked with unweighted CSPs, where fj(x) ∈{0,1}. In this problem, we will work with weighted CSPs, which associates a weight for each assignment x based on the product of m factor functions f1,…,fm:
m
Weight(x) = Yfj(x)
j=1
where each factor fj(x) ≥ 0. Our goal is to find the assignment(s) x with the highest weight. As in problem 0, we will assume that each factor is either a unary factor (depends on exactly one variable) or a binary factor (depends on exactly two variables).
For weighted CSP construction, you can refer to the CSP examples we provided in util.py for guidance (create_map_coloring_csp() and create_weighted_csp()). You can try these examples out by running
python run_p1.py
Take a look at BacktrackingSearch.reset_results() to see the other fields which are set as a result of solving the weighted CSP. You should read submission.BacktrackingSearch carefully to make sure that you understand how the backtracking search is working on the CSP.
Problem 1a [4 points] ¥
Let’s create a CSP to solve the n-queens problem: Given an n × n board, we’d like to place n queens on this board such that no two queens are on the same row, column, or diagonal. Implement create_nqueens_csp() by adding n variables and some number of binary factors. Note that the solver collects some basic statistics on the performance of the algorithm. You should take advantage of these statistics for debugging and analysis. You should get 92 (optimal) assignments for n = 8 with exactly 2057 operations (number of calls to backtrack()).
Hint: If you get a larger number of operations, make sure your CSP is minimal. Try to define the variables such that the size of domain is O(n).
Problem 1b [4 points] ¥
You might notice that our search algorithm explores quite a large number of states even for the 8 × 8 board. Let’s see if we can do better. One heuristic we discussed in class is using most constrained variable (MCV): To choose an unassigned variable, pick the Xj that has the fewest number of values a which are consistent with the current partial assignment (a for which get_delta_weight() on Xj = a returns a non-zero value). Implement this heuristic in get_unassigned_variable() under the condition self.mcv = True. It should take you exactly 1361 operations to find all optimal assignments for 8 queens CSP — that’s 30% fewer!
Some useful fields:
• csp.unaryFactors[var][val] gives the unary factor value.
• csp.binaryFactors[var1][var2][val1][val2] gives the binary factor value. Here, var1 and var2 are variables and val1 and val2 are their corresponding values.
Hint: you can simply use get_delta_weight() rather than csp.unaryFactors and csp.binaryFactors.
Problem 1c [5 points] ¥
You should make sure that your existing MCV implementation is compatible with your AC-3 algorithm as we will be using all three heuristics together during grading.
With AC-3 enabled, it should take you 769 operations only to find all optimal assignments to 8 queens CSP — That is almost 45% fewer even compared with MCV!
Hint 1: documentation for CSP.add_unary_factor() and CSP.add_binary_factor() can be helpful.
Problem 2: Handling n-ary factors
So far, our CSP solver only handles unary and binary factors, but for any non-trivial application, we would like to define factors that involve more than two variables. It would be nice if we could have a general way of reducing n-ary constraint to unary and binary constraints. In this problem, we will do exactly that for two types of n-ary constraints.
The second type of n-ary factors is constraints on the sum over n variables. You are going to implement reduction of this type.
Problem 2a [5 points] ¥
Let’s implement get_sum_variable(), which takes in a sequence of non-negative integervalued variables and returns a variable whose value is constrained to equal the sum of the variables. You will need to access the domains of the variables passed in, which you can assume contain only non-negative integers. The parameter maxSum is the maximum sum possible of all the variables. You can use this information to decide the proper domains for your auxiliary variables.
How can this function be useful? Suppose we wanted to enforce the constraint [X1+X2+X3 ≤ K]. We would call get_sum_variable() on (X1,X2,X3) to get some auxiliary variable Y , and then add the constraint [Y ≤ K]. Note: You don’t have to implement the ≤ constraint for this part.
Problem 2b [4 points] ¥
Let’s create a CSP. Suppose you have n light bulbs, where each light bulb i = 1,…,n is initially off. You also have m buttons which control the lights. For each light bulb i = 1,…,n, we know the subset Ti ⊆{1,…,m} of buttons that control it. When button j is pressed, it toggles the state of light bulbs whose corresponding T includes j (For example, assume 1 ∈ T3 and light bulb 3 is off, then after the first button is pressed, light bulb 3 will be on). In code, Ti corresponds to buttonSets[i] (i ranges from 0 to n−1). Your goal is to turn on all the light bulbs by pressing a subset of the buttons. Implement create_lightbulb_csp to solve this problem.