Learning to relate terms in a multiple agent environment
In the first part of the paper we describe how different agents can arrive at different (but overlapping) views of reality. Although the agents can cooperate when answering queries, it is often desirable to construct an integrated theory that explains ‘best’ a given reality. The technique of knowledge integration based on an earlier work is briefly reviewed and some shortcomings of this technique are pointed out. One of the assumptions underlying the earlier work was that all agents must use the same predicate vocabulary. Here we are concerned with the problems that can arise if this assumption does not hold. We also show how these problems can be overcome. It is shown that standard machine learning techniques can be used to acquire the meaning of other agent's concepts. The experiments described in this paper employ INTEG.3, a knowledge integration system, and GOLEM, an inductive system based on relative least general generalization.
Keywordsknowledge integration language differences learning concept definitions learning unknown concepts predicate vocabulary learning in distributed systems
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