In this section we present a method of knowledge integration [Brazdil&Torgo,1990]. The main purpose of this method is as follows. Given a set of agents, each one involved in producing a theory, try to integrate the individually obtained theories into one integrated theory(IT) that performs better than any of the individual theories. Those agents can obtain their theories in no matter way, as long as they are expressed in the same agreed, integration language. Also the different theories should address the same problems, so that a performance gain can be obtained when joining the individual's expertise.
In the experiments described later, a system called INTEG.3 is used. In those experiments two different machine learning algorithms were used to create the individual agent's theories. Each theory is created using its own empirical evidence (examples). So the individual learning phase is done completely independently from the point of view of the agents. Then system INTEG.3 using the individual theories obtained from the agents builds an IT, which we verified that performed better than the initial individual theories.
During the process of integration the rules learned by all agents are evaluated and using this evaluation INTEG.3 decides which rules to include in the IT and which are to be forgotten. The process of evaluation is done using a set of examples (DI) which INTEG.3 uses to observe each agent's rule performance. The evaluation of the rules is done via quantitative and qualitative characterization. With this characterization which is described below system INTEG.3 obtains what we call the rules quality.