CSB551 – Elements of Artificial Intelligence – Fall 2009_1_

Document Sample
CSB551 – Elements of Artificial Intelligence – Fall 2009_1_ Powered By Docstoc
					            CSB551 – Elements of Artificial Intelligence – Fall 2009

                            Homework #3
                       (CSPs and Game Playing)
                        Due: 10/13/09 (5:15 pm)

How to complete this HW: Either 1) type your answers in the empty spaces below
each problem and print out this document, or 2) print this document and write your
answers in the empty spaces on the printout. Return the homework in class, during office
hours, or slip it under the door of Lindley 301F before 5:15 on Tuesday, 10/13/09.

Your name:

Your email address:

Note on Honor Code: You must NOT look at previously published solutions of any
of these problems in preparing your answers. You may discuss these problems with other
students in the class (in fact, you are encouraged to do so) and/or look into other
documents (books, web sites), with the exception of published solutions, without taking
any written or electronic notes. If you have discussed any of the problems with other
students, indicate their name(s) here:
Any intentional transgression of these rules will be considered an honor code violation.

General information: Justify your answers, but keep explanations short and to the
point. Excessive verbosity will be penalized. If you have any doubt on how to interpret a
question, tell us in advance, so that we can help you understand the question, or tell us
how you understand it in your returned solution.

        Problem#               Max. grade             Your grade
        I                      30
        II                     30
        III                    20
        IV                     20
        Total                  100
I. Constraint Satisfaction: Tiling with Dominoes (30 points)
Consider the problem of tiling a planar region R with n dominoes. Each domino is a 21
rectangle. R is an arbitrary collection of 2n 11 squares. Figure 1 shows one example of
such a region. The squares are numbered 1 through 2n. R is described by the set of all the
pairs (a,b), a, b  {1, 2, …, 2n}, a < b, such that square a and square b are edge-
connected (i.e., have an edge in common). In this representation, let R = {p1, p2, …, pr},
where each pk, k = 1 to r, is a pair of edge-connected squares.

                    Figure 1: Example of a region R where 2n = 16.
1. Formulate this problem as a constraint-satisfaction problem (CSP) where the
   dominoes are the variables, that is, define the variable domains and the constraints.
2. Formulate this problem as a CSP where the squares are the variables. In this
   formulation, keep the size of the domains as small as possible (4 values at most) by
   noticing that it does not matter which particular domino goes on a given pair of
3. Construct two connected regions with 6 squares each, such the constraint-graph of the
   CSP formulated as in the above Question 2 is tree-structured for one region and not
   tree-structured for the other.
4. Consider the region R shown in Figure 1. We want you to solve the corresponding
   CSP formulated as in Question 2 using the backtracking search algorithm with
   forward checking and the most-constrained-variable heuristic. We ask you to report
   what each step of the algorithm performs in the following format:
         Step 1 (choice of variable and value): Here tell us which variable is selected
              and which value is assigned to it.
         Step 2 (forward checking): Here tell us what are the variables whose domains
              are reduced and what are their new domains.
         Step 3 (choice of variable and value): …
         Step 4 (forward checking): …
         Etc …

   When there is a tie among several variables using the most-constrained-variable
   heuristic, select the variables in increasing numerical order (of the squares). When
several values can be assigned to a variable, select the values in increasing numerical
order. We ask you to only fill in the first 12 steps. For Steps 1 and 2, briefly justify
your answer. For the other steps, just give your answer.
II. Constraint Propagation: (30 points)
Alice, a junior computer science student, decides that she wants to get a minor in math.
She has already taken many of the requirements as part of her CS degree; all that remains
are the following courses. She must take both Analysis (A) and Analysis II (B). She then
has three electives of which she must take two; these are: Linear Algebra (C), Number
Theory (D) and Modern Algebra (E).

Alice has three more semester of study to go. Fortunately, every course is offered every
semester, but she can take at most two math courses during any given semester. Of
course, there are constraints on how she must take the courses. Obviously she must take
Analysis (A) before Analysis II (B). She must also take Linear Algebra (C) before
Modern Algebra (E). She can only take Number Theory (D) once she's taken Modern
Algebra (E). Sadly, Analysis (A) and Linear Algebra (C) are always offered at exactly
the same time, so she cannot take both in the same semester.

We will formulate Alice's scheduling problem as a CSP in which each variable
corresponds to a class that Alice must take, and each variable can take values
corresponding to the numbering of the remaining semesters:
     1 for junior Spring
     2 for senior Fall
     3 for senior Spring
      for “does not take this course.”

1. Find an assignment of the variables to values (just by trial and error) that satisfies the
2. State the domain of the variables and the constraints in mathematical notation using the
following symbols: =; ; ; ; <; >.
3. We will now solve this CSP using backtracking search with AC3 and forward
checking, as follows.
        (a) What are the remaining domains after calling AC3 on the initial CSP with no
        (b) What are the remaining domains after assigning A = 1 and using forward
        (c) What about after assigning A = 1 and B = 2?
        (d) Continue to solve the CSP, listing the steps of the algorithm as in Question I.4.
        Assign variables in alphabetical order. When assigning values, order the values
        from lowest to highest. (In other words, do not use the most-constrained-variable,
        most-constraining-variable, and least-constraining value heuristics)
III. Alpha-Beta Pruning (20 points)
Consider the following game tree, in which static scores are from the first player’s point
of view:

A is the root. Its children are B and C. B’s children are D, E, and F. Etc … The score
(value of the static evaluation function) of J is -3, that of K is 5, …

1. Suppose the first player is the maximizing player. Write down in the table below the
   final  or  value computed at each node by the alpha-beta pruning procedure. [If no
   value has been computed at a node, leave the entry of the table empty.] What move
   will MAX make?

            A       B        C       D       E       F       G        H       I

2. What nodes would not be examined using the alpha-beta pruning technique? (Assume
   that at each level the nodes are considered in the order listed above from left to right.)

3. Assume that alpha-beta technique keeps track of the tree path that is responsible for
   the final alpha value of the root of the tree. This path connects the root of the tree to
   some leaf node, which we call x. What is this node (e.g., x = K)?

4. Assume the maximizing-player generates a sub-tree (singular extension) from the leaf
   node x that you have identified in Question 3, and gets the following sub-tree:
Should the maximizing player change decision at A [Answer by just yes or no]?
If there is a new leaf node that is responsible for the final alpha value of the root of
the tree, what is this node? [Don’t provide any explanation.]
IV. Adversarial Search (20 points)
Consider the following two-player game on the board depicted below, with 4 squares
arranged in a line and numbered 1 through 4.

Each player has a single token. Player A starts with his token at location 1 and player B
with his token at location 4. Player A moves first. The two players take turns. When it is
X’s turn to play (where X = A or B), he must move his token to an open adjacent location
in either direction. If the opponent’s token is at an adjacent location, then X may jump
over his opponent’s token to the next location, if any. For example, if A is at 3 and B at 2,
then A may move to 1. The game ends when either A’s token reaches location 4 (then A
wins) or when B’s token reaches location 1 (then B wins). The value of the game for a
player is +1 when he wins and 1 when he loses.
[Read Question 2 before answering Question 1.]
1. Draw the complete game tree, using the following conventions:
    Write each node as (a,b), where a is the location of A’s token and b is the location
      of B’s token.
    Put the terminal nodes in square boxes and annotate each with the game value
      from A’s point of view in a circle.
    Put loop nodes (states that already appear on the path from the root) in double
      square boxes.

2. Mark each non-terminal, non-loop node in the tree you draw in Question 1 with its
   backed-up minimax value in a circle. Since the regular Minimax procedure seen in
   class does not handle loop nodes, you do not need to assign minimax values to loop
   nodes. However, explain below how you handle them. Is there any non-terminal, non-
   loop node for which you can’t back-up a minimax value?
   In the end, each node in your game graph should appear in one of the following

   In addition some nodes may have no backed-up values.
[Write the backed-up values on the tree that you drew for Question 1 and answer the
remaining of Question 2 below.]

Shared By: