-$P(burglary)\cdot\left(
- P(\text{first house is holmes'})+
- P(\text{second house is holmes'})+
- P(\text{third house is holmes'})\right)=\\
-0.5102041\cdot\left(
+Extending the problem with multiple houses, dependencies and cold night we get
+the following AILog representation:
+\inputminted[linenos,fontsize=\footnotesize]{prolog}{./src/burglary.ail}
+When thinking about the dependencies and successful burglaries we found out that
+there are only four possible successful burglaries. In the model we abstracted
+from the dependency layer and implemented the model in three layers. The first
+layer is the initial probability of every burglar. The second layer is the
+possible groups that lead to a successful burglary. The chances that Holmes'
+house is hit is the third layer. This results in the following probability for
+a burglary in Holmes' house.
+
+$P(burglary)\cdot(
+ P(\text{first house Holmes'})+
+ P(\text{second house Holmes'})+
+ P(\text{third house Holmes'}))=\\
+0.655976676\cdot\left(