Using Belief Theory to Diagnose Control Knowledge Quality. Application
  to cartographic generalisation
  Both humans and artificial systems frequently use trial and error methods to
problem solving. In order to be effective, this type of strategy implies having
high quality control knowledge to guide the quest for the optimal solution.
Unfortunately, this control knowledge is rarely perfect. Moreover, in
artificial systems-as in humans-self-evaluation of one's own knowledge is often
difficult. Yet, this self-evaluation can be very useful to manage knowledge and
to determine when to revise it. The objective of our work is to propose an
automated approach to evaluate the quality of control knowledge in artificial
systems based on a specific trial and error strategy, namely the informed tree
search strategy. Our revision approach consists in analysing the system's
execution logs, and in using the belief theory to evaluate the global quality
of the knowledge. We present a real-world industrial application in the form of
an experiment using this approach in the domain of cartographic generalisation.
Thus far, the results of using our approach have been encouraging.
