Added an `AbstractDecisionMaker` abstract base class which can be
implemented in order to define a decision making loop.

A concrete implementation, in the form of a `DecisionMaker` class,
has been added. At its heart it has two core methods:
1. `ask` which is used to get a point to be queried next.
2. `tell` which is used to tell the `DecisionMaker` about new
   observations. In a typical decision making setup this will result in
   the datsets and posteriors being updated.

In addition to this, in the `DecisionMaker` a `run` method is provided,
which will automatically run the decision making loop for n steps. After
the `ask` step, the functions in the `post_ask` list will be executed,
taking as arguments the decision maker and the point chosen to be
queried next. Similarly, after the `tell` step, the functions in
the `post_tell` list are executed, taking the decision maker as the sole
argument.

Implemented minor CR feedback and renamed acquisition functions to
utility functions to reflect the fact that the package will be used for
general Bayesian decision making and not just Bayesian optimisation.