Induction and the Organization of Knowledge

This chapter investigates various forms of the induction principle in view of discussing the importance of a well-known objection to induction, namely Hempel's paradox. This paradox arises because an inductive hypothesis can be confirmed as well by its instances (e.g., confirming that all crows are black by actually seeing a black crow) as by "negative" information (e.g., confirming that all crows are black by actually seeing a non-black non-crow entity such as a white shoe). In spite of its apparent simplicity, this paradox is still an important issue that should be considered by systems performing unsupervised learning since they are prone to confirm their hypotheses by such irrelevant negative information. It is argued here that avoiding Hempel's paradox can be achieved by a tight integration of the statistical findings together with a clear organization of knowledge. Progressive organization of knowledge plays an essential role in the inductive growth of new scientific theories. In that sense, multistrategy learning, that integrates numerical induction and building knowledge structures is compulsory in order to avoid confirmation by non relevant information.