Outline

• Sudoku Example
• Quiz
• Backtracking Search For CSPs

Introduction

• We were talking about constraint satisfaction problems.
• These consisted of a set of variables, a set of domains for these variables, and a set of constraints (pairs of variables and relations) that these variables should satisfy.
• A solution to such a problem was an assignment of values from the domains to each of the variables such that these values satisfy all the constraints.
• We formulated map-coloring, job-shop scheduling, and the n-queens problems as CSPs.
• We looked at different common kinds of constraint people have considered.
• We also looked at local consistency checks such as node consistency, arc consistency, n-consistency which can be done with reasonably fast algorithms to do constraint propagation to narrow down the search space that a search algorithm might need to consider in finding solutions.
• Today, we begin by looking at sudoku formulated as a CSP and see how to do constraint propagation for this particular case.

Sudoku

• A Sukodu board is a 9x9 board, 81 squares, some of which are initially filled with values from 1to 9.
• The goal is to fill in all the remaining squares such that no digit appears twice in any row, column, or 3x3 box.
• A row, column, or box is called a unit.
• A CSP solver can typical solve any such problem in under a 1/10 of a second.

Formulating Sudoku as a CSP

• A Sudoku puzzle can be considered a CSP with 81 variables, one for each square.
• We label there `A1`,...`A9` for the top row to `I1`, ..`I9` for the bottom row.
• The empty squares have domain `{1,2, ..., 9}`.
• The pre-filled squares have a domain consisting of a single value.
• In addition, there are 27 different Alldiff constraints, use for each unit: `Alldi\f\f(A1,A2,A3,A4,A5,A6,A7,A8,A9)`
  `Alldi\f\f(B1,B2,B3,B4,B5,B6,B7,B8,B9)`
  `...`
  `Alldi\f\f(A1,B1,C1,D1,E1,F1,G1,H1,I1)`
  `Alldi\f\f(A2,B2,C2,D2,E2,F2,G2,H2,I2)`
  `...`
  `Alldi\f\f(A1,A2,A3,B1,B2,B3,C1,C2,C3)` (top left 3x3 square)
  `...`

Effects of consistency checks on Sudoku

• Notice we can expand the Alldiff's into `((n),(2))` binary constraints. For example the first row constraint, becomes `A1 ne A2`, `A1 ne A3`, ..., `A1 ne A9`, `A2 ne A3`, ...
• So we can apply the AC-3 algorithm on the result.
• Consider `E6` in the left hand board above. From the constraints in the box, we can remove 2,8, 1, 7 from the domain of `E6`.From the constraints on the column, we can eliminate 5, 6, 2, 8, 9, and 3. This leaves E6 with a domain of {4}. So we know the answer for `E6`.
• Continuing in this fashion with other unfilled squares, it turns out that we can exactly fix all the values in the above puzzle.
• So just running AC-3 on the above solves the puzzle.

Quiz

Backtracking Search for CSPs

• Unlike the Sudoku example, many CSPs cannot be solved by inference alone -- you also need to search for solutions.
• One possible way to do such a search would be to do a depth-limited search: A state would be a partial assignment, and an action would be adding `var=value` to the assignment.
• Unfortunately, for a CSP with `n` variables of domain size `d`, the branching at the top level is `nd`, at the second level `(n-1)d` ... So for `n` levels the tree has `n!d^n` leaves, even though there are only `d^n` possible complete assignments!
• Notice the actions in a CSP are commutative. i.e., the order of any given set of actions has no effect on the outcome.
• Therefore, we only need consider a single variable at each node in the search tree.
• So, for example, in the color the map of Australia problem, at the root we might make the choice between `SA = r\e\d`, `SA = green`, and `SA=blue`.
• With this restriction, the number of leaves is `d^n`, as one would expect.
• We call the variant of depth-first search that chooses values for one variable at a time and backtracks when a variable has no legal values left to assign backtracking search.

Backtracking-Search

Here is some pseudo-code for backtracking search:

function Backtracking-Search(csp) returns a solution, or failure
    return Backtrack({},csp)

function Backtrack(assignment, csp) returns a solution, failure
   if(assignment is complete) then return assignment
   var := Select-Unassigned-Variable(csp)
   for each value in Order-Domain-Values(var, assignment, csp) do
        if value is consistent with assignment then
            add {var= value} to assignment
            inferences := INFERENCE(csp, var, value) 
                //this might do AC-3 or the like
            if inferences != failure then
                add inferences to assignment
                result := Backtrack(assignment, csp)
                if result != failure then
                    return result
        remove {var = value} and inferences from assignment
   return failure

Making Backtracking Search More Sophisticated

Performance of backtracking can be improved by focusing on:

• Which variable should be assigned next (Select-Unassigned-Variable) and in what order should its values be tried (Order-Domain-Values)?
• What inferences should be performed at each step in the search (Inference)?
• When the search arrives at an assignment that violates a constraint, can the search avoid repeating this failure?

Variable and Value Ordering

• The simplest strategy for Select-Unassigned-Variable is to choose the next assignment unassigned variable in order:` {X_1, X_2, ...}`.
• This static variable ordering is often not very efficient. For example, after the assignment WA=red and NT=green there is only one possible value for SA, so it makes sense to assign SA=blue next rather than assign say the variable Q, even though Q might be next in the variable number ordering. This is because, by setting SA = blue we might force the values of further variables.
• This idea can be made into a heuristic. Choose the variable from amongst those that have the fewest "legal" values. This is called the minimum-remaining-value (MRV) heuristic. It is also known as the most-constrained variable heuristic and the fail-first heuristic.
• MRV doesn't help to choose the first region to color in the map of Australia.
• One heuristic one can use for this is the degree heuristic: choose the variable that is involved in the largest number of constraints on other unassigned variables. SA is the variable with the highest degree for Australia at 5.
• Using the degree heuristic on Australia, actually solves the problem without having to do backtracking.
• Once the variable has been selected, one then needs to choose the order to examine the values one could assign it.
• This can be done using the least-constraining-value heuristic. Pick the value that rules out the fewest choices for the neighboring variables in the constraint graph.

Interleaving search and Inference

• So far we have seen how AC-3 and other algorithms can infer reductions in the domain of variables before we begin the search.
• It can be even more powerful in the course of search: every time we make a choice of a value for a variable, we have a new opportunity to infer domain reductions.
• Forward checking is one way to implement Inference in the backtracking algorithm: Whenever a variable `X` is assigned we then do arc consistency for it: for each unassigned variable `Y` that is connected to `X` by a constraint, delete from `Y`'s domain any value that is inconsistent with the value chosen for `C`.

Intelligent Backtracking

• Our backtracking algorithm currently has a very simple policy of what to do when a branch of the search fails: back up to the preceding variable and try a different value for it.
• This is called chronological backtracking. There are other possible ways to do this which sometimes work better.
• Suppose in the Australia problem, we have the partial assignment {Q=red, NSW = green, V=blue, and T=red}.
• When we try to assign SA, we see that every value violates a constraint.
• Since T was the last assigned, we back up and try to assign a new color for T and then proceed to fail in exactly the same way.
• A more intelligent approach would be to backtrack to a variable that was responsible for making one of the possible values of SA impossible.
• To do this, we can keep track of a set of assignments that are in conflict with some value for SA.
• The set {Q=red, NSW = green, V=blue} is called the conflict set for SA.
• The backjumping method backtracks to the most recent assignment in the conflict set (so wouldn't consider Tasmania).