diff --git a/assets/glossary.tex b/assets/glossary.tex
index 4c9274e..62114a7 100644
--- a/assets/glossary.tex
+++ b/assets/glossary.tex
@@ -276,6 +276,11 @@
   description={},
 }
 
+\newglossaryentry{propagator}{
+  name={propagator},
+  description={},
+}
+
 \newglossaryentry{reification}{
   name={reification},
   description={},
diff --git a/chapters/2_background.tex b/chapters/2_background.tex
index c23630b..0efdd29 100644
--- a/chapters/2_background.tex
+++ b/chapters/2_background.tex
@@ -633,9 +633,26 @@ It is clear that when the value \mzninline{a} is known, then the value of
 performing an intelligent search by inferring which which values are still
 feasible for each \gls{variable} \autocite{rossi-2006-cp}.
 
+To find a solution to a given \gls{csp}, a \gls{cp} \gls{solver} will perform a
+depth first search. At each node, the \gls{solver} will try and eliminate any
+impossible value using a process called \gls{propagation}. For each
+\gls{constraint} the \gls{solver} has a chosen algorithm called a
+\gls{propagator}. Triggered by changes in the \glspl{domain} of its
+\glspl{variable}, the \gls{propagator} will analyse and prune the any values
+that are proven to be inconsistent.
+
+When there are no more triggered \glspl{propagator} and not all \glspl{variable}
+have been assigned a value, the \gls{solver} has to make a search decision. It
+will bind a \gls{variable} to a value or add a new \gls{constraint}.
 
 \begin{example}%
 \label{ex:back-nqueens}
+Consider as an example the N-Queens problem. Given a chess board of size
+\(n \times n\), find a placement for \(n\) queen chess pieces such that no queen
+can attack another. Meaning only one queen per row, one queen per column, and
+one queen per diagonal.
+
+The problem can be modelled as a \gls{csp} as follows.
 
 \begin{mzn}
   int: n;
@@ -646,9 +663,31 @@ feasible for each \gls{variable} \autocite{rossi-2006-cp}.
   constraint all_different([q[i] - i | i in 1..n]);
 \end{mzn}
 
+Since we know that there can only be one queen per column, the decision in the
+model left to make is, for every queen, where in the column the piece is placed.
+The \glspl{constraint} in the model model the remaining rules of the problem: no
+two queen can be placed in the same row, no two queen can be place in the same
+upward diagonal, and no two queens can be place in the same downward diagonal.
+This model can be directly used in most \gls{cp} \glspl{solver}, since integer
+\glspl{variable} and a \gls{propagator} are common.
+
+When solving the problem, initially no values can be eliminated from the
+\glspl{domain} of the \glspl{variable}. The first propagation will happen when
+the first queen is placed on the board. The search space will then look like
+this:
+
+\jip{insert image of nqueens board with one queen assigned.}
+
+Now that one queen is placed on the board the propagators for the
+\mzninline{all_different} constraints can start propagating. They can eliminate
+all values that are on the same row or diagonal as the queen placed on the
+board.
+
+\jip{insert previous image with all impossible moves marked red.}
+
 \end{example}
 
-\paragraph{Constraint Propagation}
+
 
 \subsection{Mathematical Programming}%
 \label{subsec:back-mip}
@@ -737,7 +776,7 @@ can then be rewritten as linear \glspl{constraint} using the \glspl{variable}
   integer \glspl{variable} \(q\) are used to represent where the queen is
   located in each column. To encode the \mzninline{all_different} constraints,
   the \glspl{indicator-var} \(y\) are inserted to reason about the value of
-  \(q\). The \cref{line:mip-channel} is used to connect the \(q\) and \(y\)
+  \(q\). The \cref{line:back-mip-channel} is used to connect the \(q\) and \(y\)
   \glspl{variable} and make sure that their values correspond.
   \Cref{line:back-mip-row} ensures that only one queen is placed in the same
   row.