If we imagine a community of independent agents interacting with some reality we can use KI as a supervisor agent that incrementally monitors each agent's learning process, telling him what he should consider and what he should forget. The individual agents can ask the supervisor to arrange a meeting between them. This supervisor, using some kind of knowledge integration methodology can provide the exchange of information between the agents. During this exchange of information one can imagine several forms of communication such as :- adoption of other agent's rules; forgetting some personal rule; or the modification of a rule to accommodate some 'critics' of the other agents. Notice that this last aspect is not considered in the presented knowledge integration methodology although some solutions have been proposed (see [Sian,1991] for an approach to the solution of this problem). After this discussion phase, each agent can return to his individual learning task, but it is logical to expect that they adopt the IT as it was agreed that it performed better.
This adoption phase has some interesting aspects. First, it demands that each agent uses an incremental learning algorithm, as it needs to continue learning from the adopted IT. This seems logical in a scenario as the one presented above if we consider the limitations of non-incremental learning algorithms discussed in section 2. Finally the adoption phase has one major difficulty that arises from the functional aspects of incremental learning algorithms. Such algorithms require not only a theory, from which they can continue to learn, but also a set of examples that support this theory. Even if we don't adopt the full-memory approach, we still need some examples to allow us to continue learning. If we don't have these examples the theory could be completely reformulated in the presence of a single new example, which is highly undesirable.
The difficulty of obtaining such a set of examples to support the IT arises from the fact that the rules contained in it possibly came from different sources(agents). The more logical solution to this problem is to ask to the agent responsible for the rule, the examples that support it. A problem of this solution is that if we put all the received examples together and present them to an incremental learning algorithm as a support for the IT, these examples force the algorithm to make some modifications to the theory. The problem is that examples used on learning of a specific agent's rule can induce modifications to other agent's rules. This leads us towards the problems of the above referred work of Sian.
Another possibility is that if we have a theory(IT) and we want a set of examples that support it, then we could use deduction to obtain such a set. In this case we could finish with a set of examples completely different from the examples used in learning the rules of IT, but this presents no problems. For this solution one has to decide how many examples to deduce so that the IT becomes robust to the arrival of new examples. The degree of robustness can be a function of the Q values obtained during the integration phase. Another important decision is which examples to deduce, because as we saw, we don't want any modifications to be induced by the obtained set of examples. Notice that if we adopted this solution, forgetting of examples would also be performed as we throw away all the examples used in the first individual learning phase and proceed with a single set of examples that should be the minimal set that guarantees that no modifications are induced to the IT.
Further research is needed to decide which of the presented alternatives is best in order to enable the agents to adopt the IT and proceed from it in their individual learning tasks.